Research Scholar of CSE Dept. successfully defends Thesis
20-May-2022
Mr. Abhir Bhandary, Research Scholar of Dr. Ananth Prabhu G, bearing USN 4SF17PEA01 of the Research Centre at Sahyadri, successfully defended his thesis titled 'Computer Aided Diagnosis for Early Lung Cancer Detection Using Content - Based Medical Image'
The focus of this study is on lung cancer diagnosis in its early stages. In this work, the research scholar has proposed a computer-assisted diagnosis system for lung cancer early detection. He has proposed four possible methodologies for lung cancer diagnosis. To assess lung anomalies in biomedical pictures, a modified AlexNet (MAN) is proposed. This study looks at images from two different modalities: chest Xrays and lung CT scans. On these two image datasets, the suggested MAN is tested individually.
The MAN is used to classify the chest X-Ray into normal and pneumonia classes during the initial assessment phase, and the suggested DL approach has an accuracy of >96%, which is higher than other DL techniques investigated in this work. The suggested MAN with SVM classifier has a classification accuracy of 86.47 percent, and when combined with EFT, a comparable DL framework, it gets a classification accuracy of >97 percent. The second and the third methodologies were carried out with real-time lung scans to test the suggested Possibilistic Fuzzy C means clustering for cancer detection and updated Possibilistic Fuzzy C means clustering methodologies. Various performance factors, including as true and false positive detection, accuracy, and classification time, as well as the sensitivity factor, were used to evaluate the proposed techniques. The results suggest that the proposed systems are effective.
The final methodology proposes an enhanced Lung parenchyma segmentation method based on Adaptive Thresholding, Fuzzy C Means Clustering, and Active Contour Model Segmentation. The suggested system, which is unsupervised GMM dependent segmentation followed by an adaptive border correction strategy to reduce computation time, does not require human interaction. Approach on Thoracic Computer Tomography Scans.
The external referee and all the members present appreciated the research work and gave suggestions for future work.
Members Present:
