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May 20, 2021 · This paper presents a detailed systematic review of deep learning techniques for the early detection of skin cancer. Research papers published in well-reputed journals, relevant to the topic of skin cancer diagnosis, were analyzed.
Our methodology consists of using Convolutional Neural Network (CNN) to identify and diagnose the skin cancer using the IS IC dataset containing 2637 images. The proposed model gives an accuracy of 88% for classifying the training dataset as either benign or malignant.
May 30, 2023 · Deep-learning-based methods have been proposed to assist dermatologists in the early and accurate diagnosis of skin cancers. This survey reviewed the most recent research articles on skin cancer classification using deep learning methods.
May 30, 2023 · This section overview a recently published paper on skin cancer detection using deep learning algorithms. Inthiyaz et al. [ 23] proposed a deep-learning-based automated system for classifying skin cancers trained on the Xiangya-Derm dataset that consists of 150,223 images.
This paper explores deep learning approaches applied to the early detection of skin cancer, presenting the study's findings through tools, graphs, tables, techniques, and frameworks. This enhances comprehension and contributes to the body of knowledge in this critical field.
Jul 13, 2022 · Skin Cancer Detection: A Review Using Deep Learning Techniques: International Journal of Environmental Research and Public Health: This paper provides a review of deep learning-based methods for early diagnosis of skin cancer. Integrating Patient Data Into Skin Cancer Classification Using Convolutional Neural Networks: Systematic Review
Feb 21, 2024 · The proposed model utilizes advanced deep-learning methodologies to extract complex features and patterns from skin cancer images. We enhance the learning procedure of deep learning...
