Developments in Deep Learning for the Diagnosis of Skin Cancer

Abstract- The most prevalent kind of cancer worldwide is skin cancer. Early detection is crucial since failure to discover it in the primary stage could be fatal. Even though there are distinctions within the class and a lot of parallels between classes, it is too hard to tell with the naked eye. Due of the disease's widespread occurrence, several automated deep learning-based algorithms have been developed to date to assist physicians in spotting skin lesions early on. We trained VGG19 on the HAM10000 dataset by fine-tuning the Convolutional Neural Networks (CNNs) and using pre-trained ImageNet weights. With FT, the best performance was noted. With an overall accuracy of 82.4±1.9 percent, the developed model outperformed the one employed in transfer learning. This performance could save morbidity and treatment costs by providing a second opinion and bolstering the clinician's diagnosis.

Convolutional Neural Network, dermatology, deep learning, transfer learning, fine-tuning, image classification, medical imaging, skin cancer, artificial intelligence in healthcare

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