Year: 2025
Type: article
Abstract: Skin cancer is a common cancer with a large count of patients diagnosed annually. Early diagnosis and correct classification of skin lesions may increase the likelihood of cure before it turns malignant and the cancer metastasises. In this study we have trained the system using the International Skin Imaging Collaboration (ISIC) curated datasets of a huge number of gold-standard lesion diagnosed training images from patients with different skin disease conditions. Convolutional neural networks are used in this study because of their superior performance in medical imaging. Various architecture models and data augmentation strategies are investigated to alleviate dataset imbalances and improve model resilience. The comparative study of model performance demonstrates the superiority of InceptionV3 model in terms of validation accuracy and computational efficiency.
Author: Smita Saxena
Institution: Savitribai Phule Pune University
Language: English
Subfield: Oncology
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
Funder: Savitribai Phule Pune University
OpenAlex ID: W4411747106