Izvorni znanstveni članak
https://doi.org/10.1080/00051144.2024.2396167
ACGAN: adaptive conditional generative adversarial network architecture predicting skin lesion using collaboration of transfer learning models
R. Gomathi
; Department of Computer Science and Engineering, Bannari Amman Institute of Technology, Sathyamangalam, India
*
S. Gnanavel
; Department of Computing Technologies, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, India
K.E. Narayana
; Department of Computer Science and Engineering, Rajalakshmi Engineering College, Chennai, India
B. Dhiyanesh
; Department of Computer Science and Engineering - ETech, SRM Institute of Science and Technology, Chennai, India
* Dopisni autor.
Sažetak
Skin cancer has become a serious disease which has the potential to scale up if it is not identified
earlier. It is imperative to detect and give treatment to skin cancer promptly. Diagnosing skin cancer manually takes a lot of time and it is costly, and the probability of false diagnosis has increased
due to the outstanding resemblances among various skin lesions. Enhancing the classification of
multi-class lesions of skin needs the development of investigative systems which should be automated. Data augmentation with GANs and Adaptive Conditional Generative Adversarial Network strategies improves performance. The performance is tested using balanced and unbalanced datasets. Using a proper process of augmentation of data, the suggested system attains a 94% accuracy for the VGG16, 93% for the ResNet50 and 94.25% for ResNet101. The process of collaboration of all such methods improves accuracy further to 95%. In summary, the novelty of the work lies in its holistic approach to automated skin lesion classification, incorporating advanced deep
learning models, novel data augmentation techniques and comprehensive performance evaluation on real-world datasets. These contributions collectively advance the field of computer-aided
diagnosis for the detection of skin cancer and treatment.
Ključne riječi
Skin cancer; deep learning; healthcare; neural networks; generative adversarial networks; data augmentation
Hrčak ID:
326339
URI
Datum izdavanja:
29.8.2024.
Posjeta: 0 *