Segmentation and Border Detection of Melanoma Lesions Using Convolutional Neural Network and SVM

Jadhav, Ashwin; Ghontale, Arun G.; Shrivastava, Vimal K.

doi:10.1007/978-981-13-1132-1_8

Cited by 19 publications

(14 citation statements)

References 7 publications

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“…Finally, we set a threshold on the entropy vector and eliminate all negative and zero features. The remaining features which are nonzero and higher than zero are feed to cubic SVM (Jadhav, Ghontale, & Shrivastava, ) for final classification.…”

Section: Proposed Methodsmentioning

confidence: 99%

Microscopic melanoma detection and classification: A framework of pixel‐based fusion and multilevel features reduction

Rehman

Khan

Mehmood

et al. 2020

Microscopy Res & Technique

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The numbers of diagnosed patients by melanoma are drastic and contribute more deaths annually among young peoples. An approximately 192,310 new cases of skin cancer are diagnosed in 2019, which shows the importance of automated systems for the diagnosis process. Accordingly, this article presents an automated method for skin lesions detection and recognition using pixel‐based seed segmented images fusion and multilevel features reduction. The proposed method involves four key steps: (a) mean‐based function is implemented and fed input to top‐hat and bottom‐hat filters which later fused for contrast stretching, (b) seed region growing and graph‐cut method‐based lesion segmentation and fused both segmented lesions through pixel‐based fusion, (c) multilevel features such as histogram oriented gradient (HOG), speeded up robust features (SURF), and color are extracted and simple concatenation is performed, and (d) finally variance precise entropy‐based features reduction and classification through SVM via cubic kernel function. Two different experiments are performed for the evaluation of this method. The segmentation performance is evaluated on PH2, ISBI2016, and ISIC2017 with an accuracy of 95.86, 94.79, and 94.92%, respectively. The classification performance is evaluated on PH2 and ISBI2016 dataset with an accuracy of 98.20 and 95.42%, respectively. The results of the proposed automated systems are outstanding as compared to the current techniques reported in state of art, which demonstrate the validity of the proposed method.

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Section: Proposed Methodsmentioning

confidence: 99%

Microscopic melanoma detection and classification: A framework of pixel‐based fusion and multilevel features reduction

Rehman

Khan

Mehmood

et al. 2020

Microscopy Res & Technique

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“…In (Jadhav, Ghontale and Shrivastava, 2019), authors extracted features of lesions using CNN, and then an SVM is used for classification. Without any preprocessing, the CNN eliminates the need for hand crafted features, and has been utilized in the proposed method for feature extraction.…”

Section: Related Workmentioning

confidence: 99%

Multi-features extraction based on deep learning for skin lesion classification

2022

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For various forms of skin lesion, many different feature extraction methods have been investigated so far. Indeed, feature extraction is a crucial step in machine learning processes. In general, we can distinct handcrafted and deep learning features. In this paper, we investigate the efficiency of using 17 commonly pre-trained convolutional neural networks (CNN) architectures as feature extractors and of 24 machine learning classifiers to evaluate the classification of skin lesions from two different datasets: ISIC 2019 and PH2. In this research, we find out that a DenseNet201 combined with Fine KNN or Cubic SVM achieved the best results in accuracy (92.34% and 91.71% ) for the ISIC 2019 dataset. The results also show that the suggested method outperforms others approaches with an accuracy of 99% on the PH2 dataset.

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“…Recently, important developments are proposed based on new kinds of neural networks for analyzing visual systems. CNN is a trail of deep neural networks which is usually used on image or speech analyzes in machine learning [22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Optimization of the Convolutional Neural Networks for Automatic Detection of Skin Cancer

et al. 2019

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AbstractConvolutional neural networks (CNNs) are a branch of deep learning which have been turned into one of the popular methods in different applications, especially medical imaging. One of the significant applications in this category is to help specialists make an early detection of skin cancer in dermoscopy and can reduce mortality rate. However, there are a lot of reasons that affect system diagnosis accuracy. In recent years, the utilization of computer-aided technology for this purpose has been turned into an interesting category for scientists. In this research, a meta-heuristic optimized CNN classifier is applied for pre-trained network models for visual datasets with the purpose of classifying skin cancer images. However there are different methods about optimizing the learning step of neural networks, and there are few studies about the deep learning based neural networks and their applications. In the present work, a new approach based on whale optimization algorithm is utilized for optimizing the weight and biases in the CNN models. The new method is then compared with 10 popular classifiers on two skin cancer datasets including DermIS Digital Database Dermquest Database. Experimental results show that the use of this optimized method performs with better accuracy than other classification methods.

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Segmentation and Border Detection of Melanoma Lesions Using Convolutional Neural Network and SVM

Cited by 19 publications

References 7 publications

Microscopic melanoma detection and classification: A framework of pixel‐based fusion and multilevel features reduction

Microscopic melanoma detection and classification: A framework of pixel‐based fusion and multilevel features reduction

Multi-features extraction based on deep learning for skin lesion classification

Optimization of the Convolutional Neural Networks for Automatic Detection of Skin Cancer

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