Abstract
Melanoma or malignant melanoma is a type of skin cancer that develops when melanocyte cells, damaged by excessive exposure to harmful UV radiations, start to grow out of control. Though less common than some other kinds of skin cancers, it is more dangerous because it rapidly metastasizes if not diagnosed and treated at an early stage. The distinction between benign and melanocytic lesions could at times be perplexing, but the manifestations of the disease could fairly be distinguished by a skilled study of its histopathological and clinical features. In recent years, deep convolutional neural networks (DCNNs) have succeeded in achieving more encouraging results yet faster and computationally effective systems for detection of the fatal disease are the need of the hour. This paper presents a deep learning-based ‘You Only Look Once (YOLO)’ algorithm, which is based on the application of DCNNs to detect melanoma from dermoscopic and digital images and offer faster and more precise output as compared to conventional CNNs. In terms with the location of the identified object in the cell, this network predicts the bounding box of the detected object and the class confidence score. The highlight of the paper, however, lies in its infusion of certain resourceful concepts like two phase segmentation done by a combination of the graph theory using minimal spanning tree concept and L-type fuzzy number based approximations and mathematical extraction of the actual affected area of the lesion region during feature extraction process. Experimented on a total of 20250 images from three publicly accessible datasets—PH2, International Symposium on Biomedical Imaging (ISBI) 2017 and The International Skin Imaging Collaboration (ISIC) 2019, encouraging results have been obtained. It achieved a Jac score of 79.84% on ISIC 2019 dataset and 86.99% and 88.64% on ISBI 2017 and PH2 datasets, respectively. Upon comparison of the pre-defined parameters with recent works in this area yielded comparatively superior output in most cases.
Highlights
Though the past two decades have seen promising possibilities in the treatment effectiveness and patient quality of life, cancer treatment continues to be a challenge for researchers worldwide.The incidence of skin cancer is higher than that of all other cancers combined
According to reports of the World Health Organization (WHO), skin cancer accounts for one third of all types of cancers happening worldwide with its influence only on an increase with time [1].The three most commonly reported skin cancers are basal cell carcinoma (BCC), squamous cell carcinoma (SCC) and malignant
We evaluated our proposed segmentation method against segmentation frameworks based on deep convolutional neural network (DCNN) [57], approaches with U-nets followed by histogram equalization and C-means clustering [58], segmentation done by crowdsourcing from International Skin Imaging Collaboration (ISIC) 2017 challenge results [59], simultaneous segmentation and classification using bootstrapping deep convolutional neural network model [60], segmentation using contrast stretching and mean deviation [61] and semantic segmentation method for automatic segmentation [62]
Summary
Though the past two decades have seen promising possibilities in the treatment effectiveness and patient quality of life, cancer treatment continues to be a challenge for researchers worldwide.The incidence of skin cancer is higher than that of all other cancers combined. According to reports of the World Health Organization (WHO), skin cancer accounts for one third of all types of cancers happening worldwide with its influence only on an increase with time [1].The three most commonly reported skin cancers are basal cell carcinoma (BCC), squamous cell carcinoma (SCC) and malignant. Diagnostics 2020, 10, 577 melanoma (MM), of which BCC and SCC account for non-melanocytic cancer [2].The vast majority of skin cancers are non-melanocytic. Melanin, produced by melanocytes, is a prominent skin constituent. This pigment is present in varying degrees depending upon a population’s historical exposure to the sun and is the determinant of an individual’s skin, hair and eye color
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