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Classification of Melanoma Skin Cancer Based on Transformer Deep Learning Model
Abstract
An increasing number of genetic and metabolic anomalies have been determined to lead to cancer, which is generally fatal. Cancerous cells may spread to any body part, which can be life-threatening. Skin cancer is significant cancer, and its frequency is increasing worldwide. The main subtypes of skin cancer are squamous and basal cell carcinomas and melanoma. The deep learning methods were used to detect the two primary types of tumours, malignant and benign, by using the MELANOMA dataset. The proposed system utilizes a convolutional neural network (CNN), transformer, and InceptionV3 architecture to learn and extract meaningful features from skin lesion images. The CNN model was trained on a large dataset of dermoscopic images of melanoma and benign lesions. The transformer model in deep learning refers to a neural network architecture based on the transformer architecture specifically designed for image classification tasks. Inception is an image recognition model that has been shown to attain greater than 78.1% accuracy on the ImageNet dataset.
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