Benign/Malignant Skin Melanoma Detection from Dermoscopy Images using Lightweight Deep Transfer Learning

被引:0
|
作者
Vijayakumar, K. [1 ,2 ]
Maziz, Mohammad Nazmul Hasan [2 ]
Ramadasan, Swaetha [3 ]
Balaji, G. [4 ]
Prabha, S. [5 ]
机构
[1] St Josephs Inst Technol, Dept Informat Technol, Chennai, Tamil Nadu, India
[2] Perdana Univ, Fac Med & Hlth Sci, Kuala Lumpur, Malaysia
[3] Sr Business Intelligence Engineer Perma Technol, Atlanta, GA 30342 USA
[4] MS TATA Consultancy Serv Ltd, Siruseri SEZ Unit, TCSL, SIPCOT IT Pk, Chennai 603103, TN, India
[5] SIMATS, Saveetha Sch Engn, Dept CSE, Ctr Res & Innovat, Chennai 602105, TN, India
来源
2024 INTERNATIONAL CONFERENCE ON ADVANCES IN COMPUTING, COMMUNICATION AND APPLIED INFORMATICS, ACCAI 2024 | 2024年
关键词
Skin cancer; Melanoma; deep learning; features mining; classification;
D O I
10.1109/ACCAI61061.2024.10601855
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Skin is one of the largest sensory organs in the human physiology and the abnormality in skin will lead to various complications. Skin melanoma is one of the harsh conditions and the untreated melanoma will lead to death due to the cancer. Early diagnosis and treatment is essential and the clinical level detection is done using the dermoscopy. This work aims to implement the deep learning based benign/malignant melanoma classification. The various phases in this tool includes; data collection and preprocessing, deep-features mining, features reduction using the Bat Algorithm (BA), and classification and performance verification. The proposed work considers the lightweight deep-learning tool to examine the chosen image database. During this task, the MobileNet-variants and the NasNetvariants are considered for the study. The classification executed using the MobileNetV2 provided an accuracy of 92.50% and the NasNetMobile based detection offered an accuracy of 91.50%. The serially integrated deep-features based detection helped to get 98% accuracy when the Support Vector Machine classifier is considered. This confirms that the implemented scheme provided better detection accuracy.
引用
收藏
页数:5
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