Introduction to Bone Tumor Prediction Using Deep Learning (ResNet50 & VGG19)

Introduction to Bone Tumor Prediction Using Deep Learning (ResNet50 & VGG19)

Akash Ahlawat , Kunal Rathi , Naitik Kaushik , Ashish , Akash Soam

Overview

Bone tumors, whether benign or malignant, pose significant health risks, requiring timely and accurate diagnosis for effective treatment. Traditional diagnostic methods, including biopsy and radiological imaging, rely heavily on expert interpretation, which can be time-consuming and subject to variability among radiologists. With advancements in artificial intelligence (AI) and deep learning, computer-aided diagnosis (CAD) systems have emerged as powerful tools to enhance diagnostic accuracy and reduce human error.

Deep learning models, particularly Convolutional Neural Networks (CNNs), have demonstrated remarkable success in medical image analysis, including tumor classification. Among various CNN architectures, ResNet50 and VGG19 stand out due to their high accuracy and robustness in feature extraction and classification. These models have been widely used in image-based disease detection, making them suitable for bone tumor prediction.