Deep learning in Dermatology for skin Diseases Detection
Upma Yadav1, Ashok Kumar2, Anamika Tiwari3, Saurabh Mukherjee4
1Ms Upma Yadav, Department of CS & Eng. Bhabha Institute of Technology Kanpur Dehat, India.
2Mr Ashok Kumar, Department of CS Banasthali Vidyapith Rajasthan, India.
3Miss. Anamika Tiwari, Department of CS & Eng. Bhabha Institute of Technology Kanpur Dehat, India.
4Dr Saurabh, Department of CS Banasthali Vidyapith Rajasthan, India.
Manuscript received on March 12, 2020. | Revised Manuscript received on March 25, 2020. | Manuscript published on March 30, 2020. | PP: 3929-3932 | Volume-8 Issue-6, March 2020. | Retrieval Number: F8498038620/2020©BEIESP | DOI: 10.35940/ijrte.F8498.038620
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Dermatology is a medical field that treats skin health and diseases. People feeling disease symptoms of an affecting the skin must consult a dermatologist if this stipulation does not respond to home remedy. Early detection and treatment can correct most skin disorders. Basal Cell Carcinoma (BCC), Melanoma and Squamous Cell Carcinoma (SCC) are typically appearing type of skin cancers. The purpose of this effort is to provide a system that can be deployed to classify dermatoscopic images to predict skin diseases with early detection and higher accuracy . This work is a concrete effort to accomplish higher degree of accuracy for clinical usage by implementing advances in soft computing and image processing like deep learning and in-depth neural networks in an early stage for 7 class classification for HAM10000 dataset.
Keywords: Dermatoscopic, Imaging modality, Invasive, Non-invasive.
Scope of the Article: Deep Learning.