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  Authors

Rajnish Kumar,Ronit Kumar Yadav,Saurabh Singh,Talha Helal,Prof Minal Khandare

  Keywords

Skin Cancer Classification, Deep Learning, Convolutional Neural Networks (CNNs), Compact Transformers, Radiomics, Patient Metadata, Transfer Learning, HAM10000 Dataset, ISIC-2019 Dataset, Class Imbalance

  Abstract


Skin cancer, one of the most prevalent cancers worldwide, requires timely and accurate detection for better pa- tient outcomes. This project investigates the use of deep learning methods for automatically classifying skin cancer from dermoscopic pictures. Specifically, convolutional neural networks (CNNs) and hybrid models incorporating transfer learning are employed to enhance classification accuracy. To tackle issues including unequal class distribution, noise in images, and restricted data accessibility, preprocessing methods like data augmentation, denoising, and feature extraction are implemented. The project explores novel model architectures, including compact transformers and dual autoencoder models, aiming to improve the model's performance. Additionally, established benchmark datasets such as HAM10000 and ISIC-2019 are used for training and evaluation. The effective- ness of the developed model is assessed using key metrics, includ- ing accuracy, sensitivity, and specificity. The results highlight the potential of deep learning in accurately classifying skin lesions as malignant or benign, offering valuable support for clinicians in early skin cancer diagnosis, especially in resource-limited settings.By highlighting current trends, obstacles, and potential avenues for future research in the field, this work advances automated diagnostic systems for skin cancer

  IJCRT's Publication Details

  Unique Identification Number - IJCRT2501387

  Paper ID - 275765

  Author type - Indian Author

  Page Number(s) - d436-d441

  Pubished in - Volume 13 | Issue 1 | January 2025

  DOI (Digital Object Identifier) -   

  No Of Downloads - 107

  Author Country - India, 560046, Banglore, Banglore, 560046, Science and Technology

  Publisher Name - IJPUBLICATION | www.ijcrt.org | ISSN : 2320-2882

  E-ISSN Number - 2320-2882

  Published Paper PDF : - http://www.ijcrt.org/papers/IJCRT2501387

  Published Paper URL: : - http://ijcrt.org/viewfull.php?&p_id=IJCRT2501387

  Published Paper PDF Downlaod: - download.php?file=IJCRT2501387

  Cite this article

Rajnish Kumar,Ronit Kumar Yadav,Saurabh Singh,Talha Helal,Prof Minal Khandare,   "An Extensive Analysis of Deep Learning Methods for Skin Cancer Identification", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.13, Issue 1, pp.d436-d441, January 2025, Available at :http://www.ijcrt.org/papers/IJCRT2501387.pdf

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