Refining Skin Cancer Classification using Model Augmentation in Inception V4

Skin cancer is a major global health concern that manifests as abnormal development of skin cells caused mostly by ultraviolet (UV) radiation exposure. The disease has two types: benign and malignant. Benign tumors, such as moles or seborrheic keratosis, are not malignant and cause few health problems. Malignant tumors, such as melanoma and basal cell carcinoma, can penetrate and metastasize in surrounding tissues, resulting in serious health implications and increased mortality rates. Early identification of skin cancer is critical for treatment success and patient survival. One of the promising technologies to be utilized for enhanced accuracy in diagnosing skin cancer is the advanced techniques in artificial intelligence, such as deep learning. Amongst such architectures is the complex InceptionV4 with its deep layers and feature extraction capability. It efficiently employs transfer learning, allowing the model to use pre-trained weights from big datasets, considerably reducing the need for massive labelled data in specialized medical settings. Furthermore, hyper parameter adjustment is critical for optimizing model performance, allowing for fine-tuning of parameters to reach the highest predicted accuracy. When paired with model augmentation techniques, these strategies resulted in an astonishing 84% accuracy with a training time of only 262 seconds. This improvement in performance illustrates the technologies' ability to increase diagnostic capabilities. This research investigates the integration of these sophisticated technologies in skin cancer detection, focusing on their potential to improve diagnostic accuracy and patient outcomes in the ongoing fight against skin cancer.