Skin Cancer Detection Using 3D-TDP
Dhiraj Kailas Ade, Gunjan Dinesh Rakhunde, Anup Drumod Dakare, Ayush Raju Dubey, Prof. Komal Mehesh Sharma
Dept. of Artificial Intelligence
G.H. Raisoni College Of Engineering
Nagpur,India
Abstract- Skin cancer, a critical health concern, is among the most common and potentially lethal cancers globally. Despite its prevalence, to increase survival chances, early identification is essential, like treatments are most effective when administered during the initial stages of the disease. Traditional diagnostic methods such as visual inspection or dermatoscopy, though widely used, can often lead to inconsistent and less accurate diagnoses due to the subjective nature of human assessment. The use of cutting-edge computational methods, especially deep learning, to improve the precision and effectiveness of skin cancer diagnosis has gained popularity as a means of overcoming these obstacles.
This research presents a freshly developed technique for skin cancer recognition with the use of 3D-TDP (Three-Dimensional Texture and Depth Perception) technology. The 3D-TDP method integrates spatial and textural features from skin lesion images to provide a more detailed and comprehensive analysis compared to traditional 2D methods. By capturing the depth and texture variations in the skin, the proposed system can differentiate between malignant and benign lesions with higher precision. Furthermore, the 3D-TDP model employs a deep-learning-based framework that is constructed on a sizable collection of photos of skin lesions, enabling it to automatically recognise intricate patterns linked to various forms of skin cancer.
The key advantage of the 3D-TDP approach is its ability to reduce false positives and negatives, enhancing the reliability of early-stage skin cancer detection. This system is designed to assist dermatologists by providing an automated and accurate tool that supports decision-making, especially in ambiguous cases. The results of this study demonstrate that the 3D-TDP method can significantly improve diagnostic accuracy, potentially leading to better patient outcomes and reducing the global burden of skin cancer-related deaths.
