Evaluation of deep learning models for melanoma image classification

Abstract

Melanin-producing cells are the origin of melanoma, the worst form of skin cancer (Melanocytes). If this cancer is not caught early, it might spread to other organs. With automated diagnostic technologies, clinicians and non- professionals may better diagnose diseases. Dermoscopic analysis, biopsy, and histological tests may be needed starting with a clinical assessment. Photo-based skin lesion categorization is challenging due to the fine-grained variability of skin lesions. We provide a more reliable melanoma detection model for each suspicious lesion in this paper. A set of characteristics characterizing a skin lesion's borders, texture, and coloursis used to educate convolutional neural networks. The deep learning models were generated using a standard dataset. To know the model's performance, consider the metrics like accuracy, sensitivity, specificity, Jaccard index and Dice coefficient. Transfer learning is used to categorize normal and diseased skin pictures automatically. This model-driven design helps doctors swiftly assess lesions.

Image classification-based transfer learning framework for image detection of IoT devices

“…Multiple studies in [8]- [17], [18]- [26], [27]- [29] have examined the implementation of TL in diverse applications. The works in [8]- [11] proposes TL frameworks using pre-trained convolutional neural networks (CNN) models for medical images classifications including melanoma detection, anthracnose and red-rust leaf disease detection, diabetic retinopathy identification, and pneumonia classification. Meanwhile, some CNNbased TL approaches for brain and breast tumor detection using magnetic resonance images have been investigated in [12]- [14].…”