• Vijay Arumugam

Initial and correct analysis of skin cancer is vital in decreasing mortality and morbidity connected with this dominant disease. Dermoscopic images provide a magnified outlook of skin injuries and vital information for dermatologists and researchers. This research mainly concentrated on the growth of an innovative framework for the analysis of skin cancer employing dermatoscopic images and a state-of-the-art approach in image analysis and machine learning (ML). This study develops Crow Search Algorithm with Deep Learning for Skin Cancer Diagnoses on Dermoscopy Images (CSADL-SCDDI) model. We use Bilateral filter (BF) for preprocessing to improve quality of dermatoscopic images. For feature extraction, we influence SqueezeNet technique. This model is a lightweight DL design which is well-known for its efficacy in removing significant features from images. These removed features capture serious data needed for precise skin cancer detection. The classification step is executed by utilizing Recurrent Neural Networks (RNNs). RNNs are effective and suitable for consecutive data analysis and grab time-based dependencies within feature representations which is perfect for differentiating between benign and malignant skin cancer. To improve the performance of the detection method, we use CSA for parameter tuning. CSA is a nature-inspired optimization model that enhances the hyper-parameter of RNN as well as exploits diagnostic precision method. The developed framework is estimated on a huge dataset of dermatoscopic images, representing its efficiency in skin cancer analysis. This complete technique provides excessive ability for the improvement of vigorous and precise skin cancer analytic systems with potential applications in medical settings in order to help dermatologists make decisions on time and more informative.

"Modeling of Crow Search Algorithm with Deep Learning for Skin Cancer Diagnosis on Dermoscopic Images", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 10, page no.g281-g290, October-2023, Available :http://www.jetir.org/papers/JETIR2310532.pdf

"Modeling of Crow Search Algorithm with Deep Learning for Skin Cancer Diagnosis on Dermoscopic Images", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 10, page no. ppg281-g290, October-2023, Available at : http://www.jetir.org/papers/JETIR2310532.pdf