Impact of Fuzziness for Skin Lesion Classification with Transformer-Based Model

Aim:

      The primary objective of this project is to elevate the accuracy of skin lesion classification through the implementation of Vision Transformers within the TensorFlow framework. The aim is to contribute to the field of computer-aided diagnosis systems and improve the early detection and treatment of dermatological conditions.

 Abstract:

       This research presents a novel enhancement to skin lesion classification utilizing Vision Transformers (ViT) within the TensorFlow framework. By leveraging ViT’s capabilities, the proposed methodology achieves a notable improvement in accuracy for skin lesion identification. The study introduces an innovative approach to refine the classification process, contributing to the advancements in computer-aided diagnosis systems for dermatological conditions.

Existing Method:

      Existing approaches often rely on Convolutional Neural Networks (CNNs) for skin lesion classification. While CNNs have demonstrated efficacy, this research explores the utilization of Vision Transformers, capitalizing on their superior performance in image classification tasks.

Problem Definition:

       Skin lesion classification is a critical aspect of dermatological diagnosis, demanding high accuracy for effective early-stage detection and treatment. The challenge lies in optimizing existing methods to enhance accuracy and computational efficiency.

Proposed Method:

      The proposed methodology integrates Vision Transformers into the TensorFlow framework, emphasizing the unique capabilities of ViT for image classification. To further refine the classification process, the study introduces a strategy to enhance accuracy, thereby contributing to the overall effectiveness of skin lesion identification.