Skin Cancer Classification and Segmentation Using Deep Learning | ||
| International Journal of Telecommunications | ||
| Volume 04, Issue 01, February 2024, Pages 1-23 PDF (1.97 M) | ||
| Document Type: Original Article | ||
| DOI: 10.21608/ijt.2024.280957.1045 | ||
| Authors | ||
| Mohamed Badawi* 1; Rania Elgohary2; Mostafa Tarek3; Mohamed EzzAlRegal4; Abdulrahman Ahmed3; Ahmed Samir3; Nour Ehab3 | ||
| 1Department of Artificial Intelligence, College of Information Technology, Misr University for Science and Technology (MUST), 6th of October City 12566, Egyp | ||
| 2Head of Information Technology College, MUST University. | ||
| 3Department of Artificial Intelligence, College of Information Technology, Misr University for Science and Technology (MUST). | ||
| 4epartment of Artificial Intelligence, College of Information Technology, Misr University for Science and Technology (MUST). | ||
| Abstract | ||
| This paper integrates medical science and artificial intelligence, focusing on using convolutional neural networks (CNNs) to improve skin cancer diagnosis accuracy. Given the rising global incidence of skin cancers such as melanoma and basal cell carcinoma, this research is becoming increasingly important. This study uses the HAM10000 and PH2 datasets, which are known for their diverse skin cancer images, and employs a CNN-based approach informed by previous research findings. The proposed methodology includes extensive preprocessing and augmentation to increase the dataset's variability, allowing for thorough training and evaluation. The CNN model, which was developed using advanced training methods and includes convolutional and pooling layers, is the result of previous research demonstrating the efficacy of CNNs in skin lesion detection. Furthermore, the U-NET-based segmentation model contributes to the comprehensive analysis by precisely delineating lesion boundaries, which improves the understanding of skin cancer. The CNN model's performance is evaluated using a variety of metrics, including accuracy, classification reports, confusion matrices, and segmentation-specific metrics like the Dice coefficient and IOU. These metrics provide valuable insights into the changing landscape of skin cancer diagnosis, allowing for the development of effective, precise, and accessible healthcare solutions in the dynamic field of dermatology. | ||
| Keywords | ||
| Deep learning; computer vision; skin cancer; multi-class classification; segmentation | ||
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