Abstract :

One Of The Major Health Concerns For Human Society Is Skin Cancer. When The Pigments Producing Skin Color Turn Carcinogenic, This Disease Gets Contracted. A Skin Cancer Diagnosis Is A Challenging Process For Dermatologists As Many Skin Cancer Pigments May Appear Similar In Appearance. Hence, Early Detection Of Lesions (which Form The Base Of Skin Cancer) Is Definitely Critical And Useful To Completely Cure The Patients Suffering From Skin Cancer. Significant Progress Has Been Made In Developing Automated Tools For The Diagnosis Of Skin Cancer To Assist Dermatologists. The Worldwide Acceptance Of Artificial Intelligencesupported Tools Has Permitted Usage Of The Enormous Collection Of Images Of Lesions And Benevolent Sores Approved By Histopathology. This Paper Performs A Comparative Analysis Of Six Different Transfer Learning Nets For Multi-class Skin Cancer Classification By Taking The HAM10000 Dataset.We Used Replication Of Images Of Classes With Low Frequencies To Counter The Imbalance In The Dataset. The Transfer Learning Nets That Were Used In The Analysis Were VGG19, InceptionV3, InceptionResNetV2, ResNet50, Exception, And Mobile Net. Results Demonstrate That Replication Is Suitable For This Task, Achieving High Classification Accuracies And F-measures With Lower False Negatives. It Is Inferred That Exception Net Outperforms The Rest Of The Transfer Learning Nets Used For The Study, With An Accuracy Of 90.48. It Also Has The Highest Recall, Precision, And F-Measure Values. KEYWORDS: Deep Learning, Cancer, Frequencies

Published:

02-4-2026

Issue:

Vol. 26 No. 4 (2026)

Page Nos:

194-201

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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