MARC details
| 000 -LEADER |
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| fixed length control field |
03952nam a22003017a 4500 |
| 001 - CONTROL NUMBER |
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| control field |
th644 |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
ISI Library, Kolkata |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20250911155621.0 |
| 040 ## - CATALOGING SOURCE |
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| Original cataloging agency |
ISI Library |
| Language of cataloging |
English |
| 082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Edition number |
23rd |
| Classification number |
616.0754 |
| Item number |
P153 |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Pal, Surochita |
| Relator term |
author |
| 245 10 - TITLE STATEMENT |
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| Title |
On Automated Analysis of Lung Images with Deep Learning for Healthcare/ |
| Statement of responsibility, etc |
Surochita Pal |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
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| Place of publication, distribution, etc |
Kolkata: |
| Name of publisher, distributor, etc |
Indian Statistical Institute, |
| Date of publication, distribution, etc |
2025 |
| 300 ## - PHYSICAL DESCRIPTION |
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| Extent |
xxv, 134 pages, |
| Dimensions |
ills |
| 502 ## - DISSERTATION NOTE |
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| Dissertation note |
Thesis (Ph.D) - Indian Statistical Institute, 2025 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
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| Bibliography, etc |
Includes bibliography |
| 505 ## - FORMATTED CONTENTS NOTE |
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| Formatted contents note |
Introduction 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 ## - CREATION/PRODUCTION CREDITS NOTE |
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| Creation/production credits note |
Guided by Prof. Sushmita Mitra |
| 520 ## - SUMMARY, ETC. |
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| Summary, etc |
Automated 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 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Medical Image Analysis |
| 650 #4 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Lung Disease Detection |
| 650 #4 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
COVID-19 |
| 650 #4 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Computer-Aided Diagnosis (CAD) |
| 650 #4 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Deep Convolutional Neural Networks (CNNs) |
| 650 #4 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Noninvasive Diagnosis |
| 856 ## - ELECTRONIC LOCATION AND ACCESS |
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| Uniform Resource Identifier |
<a href="https://dspace.isical.ac.in/jspui/handle/10263/7572">https://dspace.isical.ac.in/jspui/handle/10263/7572</a> |
| Link text |
Full text |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
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| Source of classification or shelving scheme |
Dewey Decimal Classification |
| Koha item type |
THESIS |
