A COMPREHENSIVE JOINT LEARNING SYSTEM TO DETECT SKIN CANCER

Authors

  • Mr.SANGALA ASHOK, V. SINDHU SREE, ALLA RADHA DEVI, AYESHA TASKEEN

Keywords:

Skin cancer detection, joint learning system, deep learning, convolutional neural networks, ensemble learning, feature fusion, automated diagnostics.

Abstract

Skin cancer detection has emerged as a critical area in medical diagnostics, with advancements in artificial intelligence (AI) and deep learning offering promising solutions. This paper presents a comprehensive joint learning system designed to enhance the accuracy and efficiency of skin cancer detection. The proposed system integrates various deep learning models and techniques to analyze skin lesion images and classify them into benign or malignant categories.

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References

Chaturvedi, S. S., Gupta, K., & Prasad, P. (2019). "Skin lesion analyser: A machine learning perspective on the classification of skin lesions." arXiv preprint arXiv:1907.03220.

Akter, R., Hossain, M., & Khan, M. A. (2024). "Hybrid deep learning for skin cancer detection using InceptionV3 and DenseNet121."

arXiv arXiv:2410.14489. preprint

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Published

2023-12-24

How to Cite

Mr.SANGALA ASHOK, V. SINDHU SREE, ALLA RADHA DEVI, AYESHA TASKEEN. (2023). A COMPREHENSIVE JOINT LEARNING SYSTEM TO DETECT SKIN CANCER. Pegem Journal of Education and Instruction, 13(4), 490–496. Retrieved from https://www.pegegog.net/index.php/pegegog/article/view/4011

Issue

Vol. 13 No. 4 (2023): PEGEGOG

Section

Article

License

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