Accurate identification and Advanced mapping for comprehensive Skin Cancer Staging

The most prevalent and dangerous kind of cancer in humans is skin cancer. Melanoma is a kind of skin cancer that is fatal. One of the deadliest cancers in the world, melanoma will spread to other body parts if it is not detected at an early stage. It is easily curable if detected in its early stages. The goal's highlights the issues of a global increase in skin cancer cases, excessive medical expenses, and an exponential rise in the effect of death from delayed diagnosis due to missed opportunities for early intervention. The biopsy procedure is the official way for melanoma diagnosis and detection. This approach can be extremely painful and time-consuming. In the modern years, deep learning techniques have shown promise in automating the detection process, offering the potential for more accurate and timely diagnoses. This system is a deep learning-based predictive algorithm that uses dermoscopic image analysis to dynamically forecast melanoma skin cancer. The main objectives of this research are to find skin cancer more accurately and quickly in its early stages. This work provides a computer-aided detection technique for melanoma early detection. Our research work suggests using InceptionV3 and Resnet50 algorithms, along with image processing techniques, to create an effective diagnosis system in this study. An image of the impacted skin is captured and subjected to a few pre-processing methods to produce an improved and smoothed image. Next, the picture is subjected to morphological, thresholding, and grey scaling techniques during the data augmentation phase. It categorizes the provided image as either Stage 1 or Stage 2 melanoma. An impressive 84.5% accuracy is attained. Clinical diagnosis of many disorders is increasingly dependent on harmless clinical computer vision or medical image processing. These methods offer an automatic evaluation of images tool for a quick and precise assessment. Overall, the suggested technique is a major improvement in the identification of melanoma, providing a dependable and effective means of early diagnosis and treatment. This strategy could transform melanoma screening procedures by utilizing deep learning, which would ultimately improve patient outcomes and lessen the burden of this fatal disease.