ADVANCED CONVOLUTIONAL NEURAL NETWORKS FOR AUTOMATED SKIN DISEASE DETECTION: A COMPREHENSIVE AI-DRIVEN APPROACH

Authors

Abror Musulmonov Inha University in Tashkent School of Computer and Information Engineering

Keywords:

Convolutional Neural Networks (CNNs), Skin Disease Detection, Dermatology AI, HAM10000 Dataset, Inception v3 Architecture, Deep Learning, Automated Diagnosis, Medical Image Classification, Data Augmentation, Transfer Learning,

Abstract

This study presents the development and application of an advanced AI framework for automated skin disease detection using Convolutional Neural Networks (CNNs). By integrating pre-trained models with diverse dermatological datasets, including the HAM10000 dataset, the framework aims to enhance diagnostic accuracy and reliability. The research details the mathematical foundation of CNNs, the fine-tuning of pre-trained architectures, and the application of state-of-the-art AI techniques such as data augmentation and cross-validation. The results demonstrate a significant improvement over existing models, with the framework showing high potential for clinical application in dermatology.

Downloads

Download data is not yet available.

References

HAM10000 Dataset: "Human Against Machine with 10,000 training images" – a large collection of multi-source dermatoscopic images of common pigmented skin lesions. Available at: https://doi.org/10.1038/sdata.2018.161

Laurent Veyssier et al., "Skin-Cancer-Classifier-Dermatologist-AI," GitHub repository. Available at: https://github.com/laurentveyssier/Skin-Cancer-Classifier-Dermatologist-AI

Mirvohid Azimov, "Dermato_AI," GitHub repository. Available at: https://github.com/mirva777/Dermato_Ai

Tschandl, Philipp, et al., "The HAM10000 Dataset: A Large Collection of Multi-Source Dermatoscopic Images of Common Pigmented Skin Lesions." Scientific Data, vol. 5, 2018. Available at: https://www.nature.com/articles/sdata.2018.161

Esteva, Andre, et al., "Dermatologist-level Classification of Skin Cancer with Deep Neural Networks." Nature, vol. 542, no. 7639, 2017, pp. 115-118. Available at: https://www.nature.com/articles/nature21056

Brinker, Titus J., et al., "Deep Learning Outperforms Dermatologists in the Classification of Pigmented Skin Lesions: A Multi-Center, Single-Blind, Randomized, Controlled Trial." European Journal of Cancer, vol. 113, 2019, pp. 47-54. Available at: https://www.sciencedirect.com/science/article/pii/S0959804919306018

Codella, Noel, et al., "Skin Lesion Analysis Toward Melanoma Detection: A Challenge at the 2017 International Symposium on Biomedical Imaging (ISBI)." IEEE Journal of Biomedical and Health Informatics, vol. 23, no. 2, 2019, pp. 501-512. Available at: https://ieeexplore.ieee.org/document/8326993

Yu, Lei, et al., "Automated Melanoma Recognition in Dermoscopy Images via Very Deep Residual Networks." IEEE Transactions on Medical Imaging, vol. 36, no. 4, 2017, pp. 994-1004. Available at: https://ieeexplore.ieee.org/document/7761618

Downloads

Published

2024-08-28

How to Cite

Abror Musulmonov. (2024). ADVANCED CONVOLUTIONAL NEURAL NETWORKS FOR AUTOMATED SKIN DISEASE DETECTION: A COMPREHENSIVE AI-DRIVEN APPROACH. International Multidisciplinary Journal for Research & Development, 11(08). Retrieved from https://ijmrd.in/index.php/imjrd/article/view/1845