A number of ongoing work towards designing an automated pigmented skin lesion classification system utilizing deep and transfer learning might save the medical … Dermatoscopy, also knows as dermoscopy is a non-invasive clinical procedure used for melanoma detection, in which physicians apply gel on the affected skin, prior to examining it with a dermoscope. In: 2018 40th annual international conference of the IEEE engineering in medicine and biology society (EMBC), pp 2575–2578. Biomed Signal Process Control 57:101792, Kadampur MA, Al Riyaee S (2020) Skin cancer detection: applying a deep learning based model driven architecture in the cloud for classifying dermal cell images. It has been observed, and hence concluded, that irrespective of the given dataset, the best classification results are obtained with the fusion of FV0–FV1–FV2, thereby validating the strength of the feature fusion approach. Let \(FV^\kappa = \{(x_1,t_1),\ldots,(x_k,t_k),\ldots,(x_N,t_N)\}\) be a set of training matrix containing N labels, where \(X \in \{x_j\}^N_{j=1} \in {\mathbb {R}}^\nu\) is a \(\nu\)-dimension feature vector, and \(T = \{t_j\}_{j=1}^N\) are the class labels with \(t_j \in [0,\,1]\) to be a binary class. Compute the overall weighted gray value against each block. Resources: SAH, MAli. This atlas is ideal for all providers who wish to sharpen their clinical acumen and gain confidence in identifying skin cancers. Chatterjee et al. Inf Sci 467:199–218, Sankar AS, Nair SS, Dharan VS, Sankaran P (2015) Wavelet sub band entropy based feature extraction method for BCI. We propose a hierarchical architecture for feature selection and dimensionality reduction, which in the initial step relies upon entropy for feature selection, followed by dimensionality reduction using neighborhood component analysis (NCA). Syst J 8:965–979, Hoshyar AN, Al-Jumaily A (2014) The beneficial techniques in preprocessing step of skin cancer detection system comparing. We impose generalized mean on membership to incorporate local spatial information and cluster information, and on distance function to incorporate local spatial information and observation information (image intensity value). Arch Dermatol 134:1563–1570, Menzies SW, Ingvar C, Crotty KA, McCarthy WH (1996) Frequency and morphologic characteristics of invasive melanomas lacking specific surface microscopic features. skin lesion classification[10]; however, the subsequent years focused mainly on image processing techniques [17–19] and techniques for fea - [20–22] In recent … IET Comput Vis 12(8):1096–1104, Harangi B, Baran A, Hajdu A (2018) Classification of skin lesions using an ensemble of deep neural networks. Skin lesion segmentation has a critical role in the early and accurate diagnosis of skin cancer by computerized systems. An addition of post log operation further refines the channel [18], \(I_c(x,y)\), compared to original, \(I_s(x,y)\). Simulations are performed on four publicly available datasets, Table 2. In dermoscopy, cancer classification is still an outstanding challenge, which is efficiently dealt with by the proposed design; discussed below. A multilevel features selection framework for skin lesion classification T Akram, HMJ Lodhi, SR Naqvi, S Naeem, M Alhaisoni, M Ali, SA Haider, ... Human-centric Computing and Information Sciences 10 (1), 1-26 , 2020 [22] implemented a hybrid framework for hair segmentation by combining convolutional and recurrent layers. }$$, $$I_{seg} = I_{(\kappa , \sigma ^2)}^{\tiny {MD}} \cap I_{\mu }^{MS}$$, \(FV \in {\mathbb {R}}^{\{1 \times 3\}} = \{FV_k^i\}\), \(FV^\kappa = \{(x_1,t_1),\ldots,(x_k,t_k),\ldots,(x_N,t_N)\}\), \(X \in \{x_j\}^N_{j=1} \in {\mathbb {R}}^\nu\), $${\mathbb {E}}(X) = - \sum _{j=1}^{N}(x_j)log \phi (x_j)$$, \({\mathbb {Q}} \in {\mathbb {R}}^{s \times m}\), \(\varpi _j = {\mathbb {Q}}x_j \in {\mathbb {R}}^s\), $${\mathfrak {D}}(x_j,x_k) = ({\mathbb {Q}}x_j - {\mathbb {Q}}x_k)^T({\mathbb {Q}}x_j - {\mathbb {Q}}x_k)$$, $$p_{jk} ={\left\{ \begin{array}{ll} \frac{\varUpsilon (-{\mathfrak {D}}(x_j,x_k)}{ \sum _{j \ne k}\varUpsilon (-{\mathfrak {D}}(x_j,x_k))} & \text{ if } j \ne k,\\ 0 & \text{otherwise}.\\ \end{array}\right. Visualization: HMJL, SAH. ... A multilevel features selection … Here max-pooling step extracts a set of maximum responses with an objective of feature reduction, as well as robustness against noise and variations. Feature sets originating from different re-trained models are consolidated to generate a fused feature set to retain most discriminant features. These aforementioned methods have been formally approved at the 2000 Consensus Net Meeting on Dermoscopy (CNMD) [7], and are widely exploited by the physicians for diagnostics. Accordingly, this article presents an automated method for skin lesions detection and recognition using pixel-based seed segmented images fusion and multilevel features reduction. Skin lesion segmentation and classification: A unified framework of deep neural network features fusion and selection Muhammad Attique Khan, Muhammad Imran … Similarly on ISIC-MSK, the accuracy achieved by [18] is 97.20%, while the proposed methodology gives 99.20%. In: 8th international symposium on image and signal processing and analysis (ISPA 2013), Nida N, Irtaza A, Javed A, Yousaf MH, Mahmood MT (2019) Melanoma lesion detection and segmentation using deep region based convolutional neural network and fuzzy C-means clustering. Diabetes is a common disease in the modern life. It comprises two fundamental units: feature extraction and classification. The focus of this book in towards the state of the art techniques in the area of image segmentation and registration. 1 - 26 CrossRef Google Scholar Journal of Systems Engineering and Electronics. We also present one possible application of the distance functions. Among many forms of human cancer, skin cancer is the most common one. To examine trends in the treated prevalence and treatment costs of nonmelanoma and melanoma skin cancers. Kyphoplasty (balloon assisted vertebroplasty) has received tremendous emphasis. This book c- pares and contrasts data and claims that differentiate kyphoplasty from percutaneous vertebroplasty. Post-processing with morphological operators. In particular we evaluate the conventional hard This hierarchical framework optimizes fused features by selecting the principle components and extricating the redundant and irrelevant data. "A multilevel features selection framework for skin lesion classification," Human-centric Computing and Information Sciences, vol. It has been observed that the models that were pre-trained by CNN architectures are powerful features representatives. Confidence interval on all selected datasets using state-of-the-art classifiers (F-KNN, ES-KNN). Cite this article. Found inside – Page iThis book presents the refereed proceedings of the 5th International Conference on Advanced Machine Learning Technologies and Applications (AMLTA 2020), held at Manipal University Jaipur, India, on February 13 – 15, 2019, and organized in ... Melanoma is considered to be one of the deadliest skin cancer types, whose occurring frequency elevated in the last few years; its earlier diagnosis A multilevel … Therefore, an algorithmic approach, rather than the latter, is preferably followed. The acquired sensitivities of a set of classes including nevus, melanoma, BCC and SK diseases are 99.01%, 98.7%, 98.87%, and 99.41%. Includes bibliographical references (leaves 81-88). Initially, the dermoscopic images are segmented, and the lesion region is extracted, which is later subjected to retrain the selected deep models to generate fused feature vectors. In this paper, we propose an adaptive principal curvature and three blood vessels segmentation methods for retinal fundus images based on the adaptive principal curvature and images derivatives: the central difference, the Sobel operator and the Prewitt operator. Firstly, by learning from the, In this paper, we propose a new method for construction of distance functions and metrics, by applying aggregation operators on some given distance functions and metrics. Later, based on the criteria of maximum information these cells are selected (summation of pixels against each cell). We propose to fine-tune the existing pre-trained models with smaller learning rate and keep weights of the initial layers frozen to avoid distortion of the complete model. The second best family in this domain is SVM—showing average classification accuracy of 93.83% and average computational time of 1.96 s. Ensemble and Tree family is not showing improved results in terms of average classification accuracy (89.87%, 84.91%), whilst, average computational time of ensemble family is 6.05 sec, but tree family is time efficient by taking only 1.57 s. Same trend is being followed in calculating AUC. In the following section, "Literature review" section, we present a detailed overview of the existing literature in this domain. recognition function of innate immunity and maximizing the between-cluster variance, a midwave blurred infrared image is segmented into a target pixel set, a background pixel set and a blurred pixel set. Supporting experiments on synthetic, medical, and real-world images are conducted. This paper proposes a novel and effective pipeline for skin lesion segmentation in dermoscopic images combining a deep convolutional neural network named as You Only Look Once (YOLO) and the GrabCut algorithm. This set includes some state of the art techniques which have been successfully used in many medical imaging problems (gradient vector flow (GVF) and the level set method of Chan et al.[(C-LS)]. Hawas et al. Nasir, M. Attique Khan, M. Sharif, I.U. J Biomed Inform 94:103190, Chatterjee S, Dey D, Munshi S, Gorai S (2019) Extraction of features from cross correlation in space and frequency domains for classification of skin lesions. Found insideThis book presents a cutting-edge research procedure in the Nature-Inspired Computing (NIC) domain and its connections with computational intelligence areas in real-world engineering applications. An accurate segmentation of pigmented lesions may improve classification results of Computer Aided Diagnosis (CAD) tools. Marginal existence or absence of different artifacts including dark corners, hair, color chart, to name but a few. We demonstrate that our approach utilizes less than 3% deep features—equivalent to 97.55% average reduction rate, and is substantively superior to state-of-the-art approaches in terms of OA. Despite some success, however, margin exists, due to which the machine learning community still considers this an outstanding research challenge. They utilized deep encoded features for hair delineation, which are later fed into recurrent layers to inscribe the spatial dependencies among the incoherent image patches. However, automatic segmentation of skin lesions in dermoscopic images is a challenging task owing to difficulties including artifacts (hairs, gel bubbles, ruler markers), indistinct boundaries, low contrast and varying sizes and shapes of the lesion images. Pattern Recognit Lett 129:293–303, Sumithra R, Suhil M, Guru DS (2015) Segmentation and classification of skin lesions for disease diagnosis. It allows recognition of sub-surface structures of the infected skin that are invisible to naked eye. For the diagnosis of melanoma, dermatologists mostly rely on ABCD rule [4], seven-point checklist [5], and Menzie’s method [6]. CoRR, abs/1608.06993, arXiv:1608.06993, Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016) Rethinking the inception architecture for computer vision. We conclude the manuscript in "Conclusion". Manual annotations of all datasets, discussed above, by dermatologists have been provided as ground truths for the evaluation purposes. Comparing with conventional techniques, we introduced a hierarchical framework of discriminant features selection followed by a dimensionality reduction step. The authors considered both spatial and spectral features of lesion region based on visual coherency using cross-correlation technique. As a result of evaluating the segmentation results for 600 dermoscopic images from the Test set of ISIC-2017 database, the semantic segmentation provides a 90.12% of accuracy, followed by segmentation using histograms and Fully Convolutional Network, with 86,47% and 81,70% of accuracy, respectively. The average annual number of adults treated for skin cancer increased from 3.4 million in 2002-2006 to 4.9 million in 2007-2011 (p<0.001). Procedia Comput Sci 45:76–85, Attia M, Hossny M, Zhou H, Nahavandi S, Asadi H, Yazdabadi A (2019) Digital hair segmentation using hybrid convolutional and recurrent neural networks architecture. The probability that the quantity \(x_j\) will be assigned a correct class label. Cent. \end{array}} \right.$$, $$I(\kappa) = \frac{1}{\left(1+\left(\frac{\sigma _{MD}}{I_c^l}\right)\right)^{\varsigma }}+\frac{1}{2\sigma _{MD}}+C$$, $$I_{(\kappa , \sigma ^2)}^{\tiny {MD}} = {\left\{ \begin{array}{ll} {1} \quad \text{if } I(\kappa) \ge \varphi _{\tiny {thresh}};\\ {0} \quad \text{otherwise}.\\ \end{array}\right. Article One of the most dangerous complications that diabetes can cause is the blood vessel lesion. Hum. Removal of hairs on the lesion, 2. Thesis (Ph. This concept is briefly defined as a system’s capability to transfer the skills and knowledge learnt while solving one class of problems to a different class of problems, (source–target relation), Fig. Dermoscopy is a noninvasive skin imaging technique that uses optical magnification and either liquid immersion or cross-polarized lighting to make subsurface structures more easily visible when compared to conventional clinical images. 10, 12 (2020). The basic purpose of applying entropy is to identify a set of unique features having natural variability, whilst entropy value tends towards 0 with minimum feature variability. Springer Nature. "A multilevel features selection framework for skin lesion classification," Human-centric Computing and Information Sciences, vol. A multilevel features selection framework for skin lesion classification. Barata et al. Additionally, it also implements an adaptive median filter to smoothen the replaced hair pixel. Table 6 presents a comparison of classification results, in terms of OA, for two different cases: (1) simple fusion approach, (2) entropy-controlled NCA (proposed). Found insideThis book presents state-of-the-art works and systematic reviews in the emerging field of computational intelligence (CI) in electronic health care. Found insideThis book gathers outstanding research papers presented at the International Joint Conference on Computational Intelligence (IJCCI 2018), which was held at Daffodil International University on 14–15 December 2018. BMC Cancer 18(1):638, Naeem S, Riaz F, Hassan A, Miguel Tavares C, Nisar R (2015) Description of visual content in dermoscopy images using joint histogram of multiresolution local binary patterns and local contrast. Segmented image from both distributions are later fused to get the resultant image. The probabilistic methods (mean segmentation and mean deviation based segmentation) are applied independently on a same image which are later subjected to image fusion in the following step. Supervision: TA, SRN. The proposed framework is detailed in "Proposed framework" section, and "Results and discussion" section contains the experimental results and discussions. The input image is preprocessed using contrast stretching for image enhancement. Most of the constraints enumerated in "Literature review" section are successfully undertaken, and a cascaded framework is proposed, which comprises four fundamental blocks: preprocessing, lesion segmentation, feature extraction and selection, and labeling/classification. Now weights are assigned to each block according to gradient magnitude. imaging. Built on this framework, a weighted ℓ2-norm regularization term is presented by weighting mixed noise distribution, thus resulting in a universal residual-driven FCM algorithm in presence of mixed or unknown noises. Let us consider a joint vector \(FV \in {\mathbb {R}}^{\{1 \times 3\}} = \{FV_k^i\}\), where \(i \in \{1,2,3\}\)—representing selected pre-trained architecture, and \(k \in \{1, 2, 3\}\) be a selected layer. 2020; 33(4):958–970. Our method is validated on diverse image modalities such as biomedical images, natural images and texture images. Prior to the feature selection and dimensionality reduction step, the extracted features from various architectural layers are concatenated. In this work, we come up with a novel framework for skin lesion classification, which integrates deep features information to generate most discriminant feature vector, with an advantage of preserving the original feature space. These classification approaches mostly rely on the extracted set of features for the training, which are broadly divided into three main levels: low, mid, and higher levels [10]. Inception-Resnet-v2 fuses the computational adeptness of the Inception units with the optimization leverage contributed by the residual connections. In this paper, we propose a sampling technique for KDE paring, i.e., the construction of a compactly represented KDE with much smaller description complexity. … The literature survey was performed keeping the main category as skin cancer melanoma and the survey included articles, journals, and conferences papers. Such findings highlight the importance of skin cancer prevention efforts, which may result in future savings to the healthcare system. Three families of state-of-the-art classifiers are utilized for classification including KNN, SVM, and Ensemble (ES). [62], is a distance metric learning algorithm which selects the projection in the projected space by optimizing the performance of nearest neighbor classifier. [36] provided a unique solution for skin lesion segmentation using global thresholding based on color features. We utilize recent deep models for feature extraction, and by taking advantage of transfer learning. Expert Syst Appl 144:113129, Ahn E, Bi L, Jung YH, Kim J, Li C, Fulham M, Feng DD (2015) Automated saliency-based lesion segmentation in dermoscopic images. Sample segmentation results are provided in Fig. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2818–2826, Szegedy C, Ioffe S, Vanhoucke V, Alemi AA (2017) Inception-v4, inception-resnet and the impact of residual connections on learning. 8 against all selected datasets and using two different classifiers (F-KNN, ES-KNN), which works best compared to others. In this work, a widely used software, Dull Razor [58], is utilized, which is capable of localizing the hair and extricate them by implementing bilinear interpolation. et al. The regions are not always specified, however, and it is often more fitting to consider them as fuzzy subjects in the image. In this paper, we elaborate residual-driven Fuzzy C-Means (FCM) for image segmentation, which is the first approach that realizes accurate residual (noise/outliers) estimation and makes noise-free image participate in clustering. According to statistics provided by the World Health Organization (WHO), almost 132,000 new cases of melanoma are reported each year worldwide. [24] proposed automatic method for segmentation. At this stage, dimensionality reduction techniques play their vital role by reducing the number of random variables and retain the resultant vectors in the lower dimensions, \(FV^{{\mathbb {S}}}\), where \((S \ll R)\). Conventional algorithms work by making an assumption that the feature characteristics of both training and testing data are quite identical and can be comfortably approximated [43]. Correspondence to 1 presents two such examples. Additionally, there is no need for preprocessing such as color calibration or artifact disocclusion. biomed. Besides, with the constraint of spatial information, the residual estimation becomes more reliable than that only considering an observed image itself. © 2008-2021 ResearchGate GmbH. [35] proposed a lesion detection and recognition methodology—built on a multi-scale lesion-biased representation (MLR) and joint reverse classification. Additionally, introduction of this pre-processing step refines images to much extent which leads to improved classification accuracy [59]. This is something we exploit in the proposed framework, entropy-controlled neighborhood component analysis (ECNCA), for skin lesion classification. These findings demonstrate that the health and economic burden of skin cancer treatment is substantial and increasing. Skin lesion analysis toward melanoma detection 2018: A challenge hosted by the International Skin Imaging Collaboration (ISIC). Advances in neural information processing systems. A series of interlinked steps needs to be followed by each channel; those steps are enumerate below: Initially, gradients are computed for each single channel using Sobel–Feldman operator, with a fixed kernel size of \((3 \times 3)\). In contrast to traditional neighboring window of fixed size and shape, the superpixel image provides better adaptive and irregular local spatial neighborhoods that are helpful for improving color image segmentation. Inception-ResNet-V2 is an extension of inception-V3, and is also trained on ImageNet database. However, in this article, we employed conjugate gradient method. Given a source domain, \({D_S} = \left\{ {\left( {x_1^S,y_1^S} \right), \ldots ,\left( {x_i^S,y_i^S} \right), \ldots ,\left( {x_n^S,y_n^S} \right)} \right\},\) where \(\left( {x_n^S, y_n^S} \right) \in {\mathbb {R}};\) with specified learning tasks, \(L _{S}\), and target domain \({D_T} = \left\{ {\left( {x_1^T,y_1^T} \right), \ldots ,\left( {x_i^T,y_i^T} \right), \ldots ,\left( {x_m^T,y_m^T} \right)} \right\}\) having learning task \(L _{T},\)\(\left( {x_n^T,y_n^T} \right) \in {\mathbb {R}}\). We utilize recent deep models for feature extraction, and by taking advantage of transfer learning. According to the high similarity between melanoma and nevus lesions, physicians take much more time to … Found inside – Page 10-55segmentation and classification of skin lesion, Microscopy Research and Technique, 82(5), 741–763, ... Brain tumor detection and classification: A framework of marker-based watershed algorithm and multilevel priority features selection, ... Addition of a contrast stretching block facilitates segmentation step in extracting lesion area with improved accuracy. Motivated by this idea, we present an unsu-pervised image segmentation method that combines comparative reasoning with graph-based clustering. Theor Comput Sci 814:74–85, MathSciNet Skin cancer, the most common cancer in the U.S., is a major public health problem. Found insideThis book constitutes the refereed proceedings of the 7th International Workshop on Machine Learning in Medical Imaging, MLMI 2016, held in conjunction with MICCAI 2016, in Athens, Greece, in October 2016. A great number of improved fuzzy c-means (FCM) clustering algorithms have been widely used for grayscale and color image segmentation. The best results were obtained by the AS and EM-LS methods, which are semi-supervised methods. image processing, feature calculation, and classification. This paper produces an improved fuzzy c-mean, Fuzzy c-means (FCM) has been considered as an effective algorithm for image segmentation. The state-of-the-art public available datasets for skin lesions are often accompanied with a very limited amount of segmentation ground truth labeling. Proposed framework is trained on three datasets, ISIC 2016, ISIC 2017, and ISIC 2018, to achieving the promising results. The goal of this volume is to summarize the state-of-the-art in the utilization of computer vision techniques in the diagnosis of skin cancer. Malignant melanoma is one of the most rapidly increasing cancers in the world. It contains 2750 images, with 2200 training and 550 testing samples. The CAD systems adopt various machine learning techniques, for example, extracting various features (color, shape, and texture) from each dermoscopic image, followed by applying a state-of-the-art classifier [8, 9]. \(D \subset {\mathbb {R}}^{(r\times c\times p)}\), \(\Big ( \big ( \psi _1(j),\ldots ,\psi _k(j) \subset \psi \big )\in {\mathbb {R}}\Big )\), \(\overset{\sim }{\psi }: \psi \rightarrow \overset{\sim }{\psi }\), $$\overset{\sim }{\psi }\triangleq \big ( \psi ^f,\psi ^{fs},\kappa (\psi ^{fs})\big ) \in {\mathbb {Z}}^3$$, \(X_{p}=\{x_{1}, x_{2},\ldots , x_{n}\}\), \(y_{p}= \{y_{1}, y_{2}, \ldots , y_{n} \}\), $${\mathbb {F}}_{i}^{l}=\sigma \left( \sum _{i=1}^{n}x_{i}^{l -1} \times \delta _{i}^{l}+ b_{l}^{j} \right)$$, $${\mathbb {F}}_{i}^{l}=max\left(z_{2i-1}^{l-1}, z_{2i}^{l-1}\right), \quad l=2\varsigma \,\forall \, \varsigma \in {\mathbb {R}}$$, $$V_{j}^{l}=Sig\left( \sum _{i=1}^{n}x_{i}^{l-1}\times \omega _{ji}^{l}\times b_{l}^{j} \right)$$, \({D_S} = \left\{ {\left( {x_1^S,y_1^S} \right), \ldots ,\left( {x_i^S,y_i^S} \right), \ldots ,\left( {x_n^S,y_n^S} \right)} \right\},\), \(\left( {x_n^S, y_n^S} \right) \in {\mathbb {R}};\), \({D_T} = \left\{ {\left( {x_1^T,y_1^T} \right), \ldots ,\left( {x_i^T,y_i^T} \right), \ldots ,\left( {x_m^T,y_m^T} \right)} \right\}\), \(\left( {x_n^T,y_n^T} \right) \in {\mathbb {R}}\), \(\left( {\left( {m,n} \right)\left| {\left( {n \ll m} \right)} \right.}
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