An intelligent web application for predicting bone cancer
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Date
2022
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Publisher
UMT Lahore
Abstract
This thesis presents a study on the use of Efficient-Net, a pre-trained convolutional neural network (CNN) architecture, for the detection of bone tumors. Bone tumors are a serious health concern and early detection is crucial for successful treatment. The study focuses on the use of EfficientNetB5 model, which has been pre-trained on a large dataset of images, to detect bone tumors from X-ray images. The performance of the model was evaluated using a dataset of X-ray images from patients with known bone tumors. The results of the study show that the pre-trained Efficient-Net model achieved a high degree of 97% accuracy, sensitivity, and specificity in detecting bone tumors in X-ray images. The study also highlights the ability of pre-trained models like EfficientNetB5 to generalize well to different datasets, which can significantly reduce the time and resources required for training models from scratch. Overall, the study demonstrates the potential of Efficient-Net, a pre-trained CNN architecture, for the early detection of bone tumors, and provides a promising direction for future research.