• Banda Ganga Bhavani
• Manogna Tej Manti
• Lalam Hema Nookambika
• Kollam Prapulla Kumar
• Dr U Sathish Kumar

Along with a few other disorders like squamous, actinic keratosis, pigmented,and vascular illnesses, melanoma is one of the most fatal skin diseases. Nevi needs to be precise. They can be difficult to diagnose because of their similar look, which requires careful inspection by a qualified healthcare professional. Almost twice as many people die from this sickness than from different types of skin cancer. While there is a rise in various forms of melanoma among young adults, the good news is that early detection leads to very high survival rates. This abstract describes a convolutional neural network (CNN) based skin disease detection system. Using high-resolution photos of common skin lesions from the HAM10000 collection, the suggested method seeks to reliably categorize a variety of skin illnesses. The model uses deep learning to classify the photographs into appropriate disease categories by identifying complex patterns and features from the pictures. It is shown through extensive testing and assessment that the CNN-based method is a reliable means of correctly identifying skin conditions. Dermatologists could benefit from this approach by using it to help with early and accurate diagnosis, which would enhance patient care and outcomes.

Melanoma, Squamous, Skin, Actinic Keratosis, Pigmented, Vascular, Deep Learning, CNN.

"Detection Of Skin Lesions Using CNN, Tensorflow and Hybrid Feature Extraction ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 4, page no.i660-i666, April-2024, Available :http://www.jetir.org/papers/JETIR2404887.pdf

"Detection Of Skin Lesions Using CNN, Tensorflow and Hybrid Feature Extraction ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 4, page no. ppi660-i666, April-2024, Available at : http://www.jetir.org/papers/JETIR2404887.pdf