ItemConference Paper

A Comprehensive Study Using Convolutional Neural Networks as a Method for Multi-class Skin Cancer Image Classification

Title
A Comprehensive Study Using Convolutional Neural Networks as a Method for Multi-class Skin Cancer Image Classification
Creator
Upreti, Kamal; Poonia, Ramesh Chandra; Verma, Rajesh; Malik, Khushboo; Kureethara, Joseph Varghese
Description
Skin disorders occur more frequently than other kinds of diseases. Skin diseases can be attributed to a number of aspects, like fungi, bacteria, viruses, allergies, and so on. The rapid advancement of healthcare centered around lasers and photonics has rendered it feasible to diagnose skin disorders in a more accurate and timely manner. However, the cost of such a diagnostic remains extremely limited and prohibitively expensive. As a result, the use of image processing methods is beneficial in the initial phases of designing a computerized dermatology screening system. The retrieval of characteristics is an extremely important step in classifying skin disorders. The use of computer vision may play a crucial role in the diagnosis of a variety of skin conditions using a variety of approaches. The strategy we have proposed is straightforward and quick and requires no expensive technology besides a computer and a camera. When applied to the inputs of a colored picture, the method is successful. After that, resize a portion of the image to retrieve attributes with a pretrained convolutional neural network. The attribute was then classified using the multi-class XGBoost program. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
Source
Lecture Notes in Electrical Engineering;Volume;1269;pp.1-11
Date
01-01-2025
Publisher
Springer Science and Business Media Deutschland GmbH
Subject
Convolutional neural network (CNN); Deep learning; International skin identification collaborations (ISIC) and image processing; Recurrent repetition convolutional (RRN); Skin cancer; Ultraviolet (UV)
Coverage
Upreti K., CHRIST University, Delhi NCR, Ghaziabad, India; Poonia R.C., CHRIST University, Delhi NCR, Ghaziabad, India; Verma R., CHRIST University, Delhi NCR, Ghaziabad, India; Malik K., CHRIST University, Delhi NCR, Ghaziabad, India; Kureethara J.V., CHRIST University, Bangalore, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISSN: 18761100; ISBN: 978-981979514-7;
Format
online
Language
English
Type
Conference paper

Citation

Upreti, Kamal; Poonia, Ramesh Chandra; Verma, Rajesh; Malik, Khushboo; Kureethara, Joseph Varghese, “A Comprehensive Study Using Convolutional Neural Networks as a Method for Multi-class Skin Cancer Image Classification,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/25677.

Output Formats