000			03952nam a22003017a 4500
001			th644
003			ISI Library, Kolkata
005			20250911155621.0
040			_aISI Library _bEnglish
082	0	4	_223rd _a616.0754 _bP153
100	1		_aPal, Surochita _eauthor
245	1	0	_aOn Automated Analysis of Lung Images with Deep Learning for Healthcare/ _cSurochita Pal
260			_aKolkata: _bIndian Statistical Institute, _c2025
300			_axxv, 134 pages, _cills
502			_aThesis (Ph.D) - Indian Statistical Institute, 2025
504			_aIncludes bibliography
505			_aIntroduction and Scope of Thesis -- Multi-Resolution Deep Ensemble Learning for Segmentation -- Weighted Deformable Deep Net for Efficient Segmentation -- Deep Ensemble with Multimodal Fusion for Classification -- Multihead Deep Classification of Lung Diseases -- Conclusions and Future Scope
508			_aGuided by Prof. Sushmita Mitra
520			_aAutomated detection and diagnosis of lung diseases through medical image analysis offers a noninvasive alternative to invasive procedures, especially considering the challenges and potential risks associated with repeat lung operations. Noninvasive image-guided diagnostic techniques, such as lung imaging, have become essential in clinical practice. This thesis focuses on the development of a computer-aided system aimed at enhancing the classification, detection, and segmentation of lung diseases, specifically caused by COVID-19 and lung tumors, leveraging advanced computational methods. Novel segmentation algorithms, such as EFMC and WDU-Net, are devised based on encoder-decoder architectures within deep convolution networks. These algorithms undergo rigorous validation against ground truth or manual segmentation by radiologists, ensuring their accuracy and reliability. The EFMC algorithm employs a selective focus mechanism with multi-resolution blocks, allowing for precise delineation of COVID-19 affected regions in lung CT scans. Its performance is validated through extensive comparison with expert annotations, demonstrating its effectiveness in capturing subtle abnormalities while accurately segmenting lung anomalies. Similarly, WDU-Net integrates weighted deformable convolution. Here the deformable convolution modules enhance its ability to capture irregular shapes and features in COVID-19 and lung tumors. Validation against manual segmentation reveals its robustness and accuracy in segmenting COVID-19 and lung tumors from CT images; thereby, showcasing its potential for aiding clinical diagnosis and treatment planning. Next automated classification of lung tumors is devised, in the multi-modal PET-CT framework, using the innovative DEMF model. The network leverages deep convolution networks, in conjunction with dimensionality reduction, to efficiently detect and classify lung abnormalities. This demonstrates superior performance in lung cancer classification across multimodal images. Finally, the DGMC is developed to enhance diagnosis and classification of diseases, by co-learning from multimodal images. Utilizing a novel multihead classifier, the DGMC can efficiently distinguish between COVID-19, tumors, and healthy slices of the lung. The input signal encompasses CT, along with EIT-processed CT scans, in order to provide a multimodal flavour. It captures granular details of the infection, while visualizing the activation regions. Together, these advancements represent significant progress in the automated analysis of lung diseases, by providing valuable tools for the early detection and diagnosis in clinical settings.
650		4	_aMedical Image Analysis
650		4	_aLung Disease Detection
650		4	_aCOVID-19
650		4	_aComputer-Aided Diagnosis (CAD)
650		4	_aDeep Convolutional Neural Networks (CNNs)
650		4	_aNoninvasive Diagnosis
856			_uhttps://dspace.isical.ac.in/jspui/handle/10263/7572 _yFull text
942			_2ddc _cTH
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