Melanoma localization and classification through faster region-based convolutional neural network and SVM

Melanoma is a lethal skin cancer disease affecting millions of people around the globe and has a high mortality rate. Dermatologists perform the manual inspection through visual analysis of pigmented skin lesions for melanoma identification at the early stage. However, manual inspection for melanoma detection is limited due to variable accuracy and lesser availability of dermatologists. Therefore, there exists an urgent need to develop automated melanoma detection methods that can effectively localize and classify skin lesions. Accurate localization and classification of the melanoma lesions is a challenging task due to the presence of low contrast information between the moles and skin part, the massive color similarity between the infected and non-infected skin portions, presence of noise, hairs, and tiny blood vessels, variations in color, texture, illumination, contrast, blurring, and melanoma size. To address these afore-mentioned challenges, we propose an effective and efficient melanoma detection method. The proposed method consists of three steps: i) image preprocessing, ii) employing Faster Region-based Convolutional Neural Network (Faster-RCNN) for melanoma localization, and iii) application of Support Vector Machine (SVM) for the classification of localized melanoma region into benign and malignant classes. Performance of the proposed method is evaluated on the benchmark ISIC-2016 dataset launched by ISBI challenge-2016 that is diverse in terms of variations in illumination, color, texture, and size of melanoma, and presence of blurring, noise, hairs, and tiny blood vessels, etc. Moreover, we have also performed a cross-dataset validation over the ISIC-2017 dataset to show the efficacy of our method in real-world scenarios. Our experimental results illustrate that the proposed framework is efficient and able to effectively localize and classify the melanoma lesion than state-of-the-art techniques.