DermAI: A Deep Learning-Based Mobile Application for Multi-type Skin Cancer Detection
- Title
- DermAI: A Deep Learning-Based Mobile Application for Multi-type Skin Cancer Detection
- Creator
- Ramasamy, Gobi; Varghese, Nisha; Sivalingam, Elangovan; Ramachandran, Selvakumar
- Description
- The significance of early skin cancer detection for effective prevention and treatment is underscored by the limitations of traditional manual diagnostic methods used by dermatologists. Leveraging Convolutional Neural Networks (CNNs) and the HAM10000 dataset, this research aims to automate skin cancer classification through dermatoscopic image analysis. The primary objective of the research is an accurate classification system identifying seven specific skin cancer types. The novelty is the deployment of the classification system using a Mobile Application - DermAI. The trained CNN model, spanning 10 epochs, achieved remarkable precision, peaking at a 97.90 percentage test accuracy during the 7th epoch. Evaluation metrics like the confusion matrix confirm its reliability in categorizing lesions, minimizing misclassifications, and validating its efficiency as a diagnostic tool. Transforming the model into TensorFlow Lite format enables seamless integration into mobile platforms, optimizing computational resources. This allows users to access prompt skin cancer classification via an Android application, fostering accessibility to preliminary assessments. Early identification facilitates timely medical intervention, a crucial factor in enhancing prognosis. Through CNNs, TensorFlow Lite, and mobile deployment, this research strives to bridge technology and healthcare accessibility, empowering individuals to proactively manage their skin health based on classification results and initiate timely discussions with healthcare professionals. 2025 IEEE.
- Source
- 2025 11th International Conference on Smart Computing and Communications: Intelligent Technologies for a Sustainable World, ICSCC 2025;
- Date
- 01-01-2025
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Subject
- CNN; dermatofibroma; intraepithelial carcinoma; melanoma; Skin cancer detection
- Coverage
- Ramasamy G., Christ University, Department of Computer Science, Bangalore, India; Varghese N., Christ University, Department of Computer Science, Bangalore, India; Sivalingam E., EBay, United States; Ramachandran S., University of Oxford, United Kingdom
- Rights
- Restricted Access; Hardcopy may be available in the library
- Relation
- ISBN: 979-833152562-0;
- Format
- online
- Language
- English
- Type
- Conference paper
Collection
Citation
Ramasamy, Gobi; Varghese, Nisha; Sivalingam, Elangovan; Ramachandran, Selvakumar, “DermAI: A Deep Learning-Based Mobile Application for Multi-type Skin Cancer Detection,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/26107.