000			15862nam a22002657a 4500
008			201210s2024 a|||f bm|| 00| 0 eng d
024	7		_a0000-0003-3060-5986 _2ORCID
040			_aEG-CaNU _cEG-CaNU
041	0		_aeng _beng _bara
082			_a610
100	0		_aSamar Ibrahim Antar Awwad Ramadan _93567
245	1		_aEnhancing Lung Cancer Detection _b: Leveraging CT Features for Identifying Nodules of Various Sizes in Chest X-Ray Images _c/Samar Ibrahim Antar Awwad Ramadan
260			_c2024
300			_a125 p. _bill. _c21 cm.
500			_3Supervisor: Prof. Mustafa Elattar
502			_aThesis (M.A.)—Nile University, Egypt, 2024 .
504			_a"Includes bibliographical references"
505	0		_aContents: Table of Contents ACKNOWLEDGEMENT................................................................................................... i ABSTRACT........................................................................................................................ ii Table of Contents............................................................................................................... iii List of Figures................................................................................................................... vii List of Tables .......................................................................................................................x List of Equations................................................................................................................ xi List of Abbreviation......................................................................................................... xiii Chapter 1 INTRODUCTION...............................................................................................1 1.1. MOTIVATION....................................................................................................1 1.2. PROBLEM DEFINITION...................................................................................3 1.3. CONTRIBUTION................................................................................................4 1.4. THESIS OUTLINES ...........................................................................................5 Chapter 2 BACKGROUND.................................................................................................7 2.1. OVERVIEW ........................................................................................................7 2.2. CLINICAL BACKGROUND..............................................................................8 2.2.1. Respiratory System..........................................................................................8 2.2.2. Lung Cancer...................................................................................................10 2.2.3. Nodule Definitions and Different size of nodules .........................................10 2.2.4. Types of Nodules (Benign vs malignant) ......................................................13 2.2.5. Statistics of Lung Cancer Spread...................................................................15 2.3. LUNG CANCER DIAGNOSIS.........................................................................16 2.4. SUMMARY.......................................................................................................18 Chapter 3 RELATED WORK ...........................................................................................19 3.1. LUNG CANCER DETECTION & SEGMENTATION IN CT........................19 3.2. LUNG CANCER DETECTION & SEGMENTATION IN X-ray....................25 Chapter 4 DATA AND DATASET CREATION..............................................................31 4.1. PUBLIC DATASETS........................................................................................31 4.1.1. Chexpert.........................................................................................................31 4.1.2. The Japanese Society of Radiological Technology .......................................31 4.1.3. Montgomery (NLM-MC)...............................................................................32 4.1.4. Covid-19 and Lung Opacity Datasets............................................................32 iv 4.1.5. LIDC-IDRI.....................................................................................................33 4.1.6. Nodules Chest X-rays (LIDC-IDRI)..............................................................34 4.2. SIMULATED DATASET .................................................................................34 4.2.1. Simulated Data for Image Enhancement .......................................................34 4.3. CREATE SYNTHESIZED 2D CXR FROM 3D THORAX CT.......................35 4.4. ANNOTATION .................................................................................................38 4.5. CORRECTION FOR BOUNDING BOX ANNOTATION AFTER AUGMENTATION........................................................................................................38 4.6. SUMMARY.......................................................................................................40 Chapter 5 PROPOSED FRAMEWORK ...........................................................................42 5.1. OVERVIEW ......................................................................................................42 5.2. IMAGE PRE-PROCESSING ............................................................................42 5.2.1. Convert CXR images to grayscale and Crop to square..................................43 5.2.2. Adaptive Histogram Equalization..................................................................44 5.2.3. Augmentation.................................................................................................45 5.2.4. Expanding Montgomery Dataset by Merging Lungs.....................................45 5.2.5. The Laplacian of Gaussian (LoG)..................................................................46 5.2.6. Gamma Correction.........................................................................................46 5.2.7. CT Pre-processing..........................................................................................47 5.3. IMAGE ENHANCEMENT...............................................................................49 5.3.1. Proposed image enhancement approach VAE + U-Net:................................49 5.4. LUNG SEGMENTATION................................................................................51 5.5. BONE SUPPRESSION .....................................................................................53 5.5.1. Segmenting Bones from DRRs Using U-Net ................................................53 5.5.2. Generating Rib and Difference images for JSRT ..........................................55 5.5.3. Proposed Latent Boosted Pix2Pix++ Architecture (LB-Pix2Pix++).............56 5.6. CLASSIFICATION ...........................................................................................60 5.7. EXPLAINABLE AI (XAI)................................................................................61 5.7.1. Class Activation Mapping (CAM).................................................................63 5.7.2. Gradient-Weighted Class Activation Mapping (Grad-CAM)........................64 5.8. LOCALIZATION..............................................................................................67 Chapter 6 EXPERIMENTAL SETTINGS ........................................................................68 v 6.1. IMAGE ENHANCEMENT...............................................................................68 6.1.1. Overview........................................................................................................68 6.1.2. VAE Objective Function Optimization and Training....................................68 6.2. LUNG SEGMENTATION................................................................................69 6.2.1. Overview........................................................................................................69 6.2.2. Loss Functions...............................................................................................70 6.2.3. Evaluation matrices........................................................................................71 6.3. BONE SUPPRESSION .....................................................................................72 6.3.1. Bone Segmentation Training .........................................................................72 6.3.2. LB-Pix2Pix++ for Bone Suppression ............................................................72 6.3.3. Evaluation Protocol........................................................................................74 6.4. CLASIFICATION .............................................................................................75 6.4.1. Without data augmentation............................................................................75 6.4.2. With applying data augmentation ..................................................................76 6.4.3. Generalization on JSRT data .........................................................................77 6.5. LOCALIZATION..............................................................................................77 6.5.1. Bounding box estimation (detection).............................................................77 6.5.2. The segmentation model (2nd technique)......................................................78 Chapter 7 RESULTS..........................................................................................................80 7.1. IMAGE ENHANEEMENT ...............................................................................80 7.1.1. Simulation of extra/ halting structures data ...................................................80 7.1.2. The training outcome .....................................................................................80 7.2. LUNG SEGMENTATION................................................................................82 7.3. TESTING OF VAEU-NET AND RESU-NET++.............................................84 7.4. BONE SUPPRESSION .....................................................................................85 7.4.1. Ablation Study ...............................................................................................85 7.4.2. Comparison to the State-of-the-Art ...............................................................89 7.5. CLASSIFICATION ...........................................................................................90 7.5.1. Without data augmentation............................................................................90 7.5.2. With data augmentation .................................................................................90 7.5.3. Generalization on JSRT data & Performance Comparison to Literature ......91 7.5.4. Grad-CAM for the JSRT dataset....................................................................92 vi 7.5.5. Testing on unseen dataset ..............................................................................95 7.6. LOCALIZATION..............................................................................................96 7.6.1. Bounding box estimation (Detection) on JSRT.............................................96 7.6.2. Testing Bounding box detection model on unseen datasets ..........................97 7.6.3. The segmentation model (2nd technique) on JSRT.......................................99 7.6.4. Testing the segmentation model on DRRs...................................................100 Chapter 8 DISCUSSION .................................................................................................103 8.1. LUNG SEGMENTATION..............................................................................103 8.2. TESTING OF VAEU-NET AND RESU-NET++...........................................104 8.3. BONE SUPPRESSION ...................................................................................104 8.4. CLASSIFICATION .........................................................................................107 8.5. LOCALIZATION............................................................................................108 Chapter 9 CONCLUSION AND FUTURE WORK........................................................110 9.1. CONCLUSION................................................................................................110 9.2. FUTURE WORK.............................................................................................111 REFERENCES ................................................................................................................112 Appendix A EXPLAINABILITY....................................................................................121 A.1. CLASS-ENHANCED ATTENTIVE RESPONSE (CLEAR)............................121 A.2. DECONVOLUTIONAL NETWORKS .............................................................121 A.3. LAYER-WISE RELEVANCE PROPAGATION (LRP)...................................123 A.4. DIFFERENCE OF LRP FROM GRAD-CAM AND CAM...............................124 A.5. LRP RULES FOR DEEP RECTIFIER NETWORKS .......................................124 A.6. DEEP LEARNING IMPORTANT FEATURES (DEEPLIFT) .........................125
520	3		_aAbstract: The high-resolution capability of thoracic Computerized Tomography (CT) scans enables detailed visualization of lung structures, making it possible to detect even small abnormalities. This level of detail is especially crucial for identifying early-stage lung nodules, which may not be visible or distinguishable in other imaging modalities. However, CT scans have limitations like cost, access, radiation exposure, and lack of portability. Chest X-rays (CXRs) are 2D projective images where objects may not appear clear and can overlap, leading to limited visibility of lung nodules. Deep learning algorithms have the potential to address these limitations. This thesis proposes leveraging CT volume features to improve the detection and localization of lung cancer in CXR, thus enabling radiologists to diagnose lung cancer using Chest X-ray images more confidently. This could be done by finding CT features that define small, medium, and large-sized nodules and projecting those features onto CXR data. The proposed detection system includes all phases and subtasks required for CXR-based lung cancer identification. Due to a scarcity of CXR images that contain different nodule sizes, feasibility of small nodules detection in CXR images is demonstrated by fine-tuning our predictive model using Digitally Reconstructed Radiographs (DRRs), created from CT projections. Our proposed method outperforms previous state-of-theart methods, in terms of Mean Intersection Over Union (mIOU) and AUC for lung cancer detection. Furthermore, the proposed method incorporates Explainable AI (XAI) module to provide physicians with trustworthy decision-making tools. It achieves AUC of 0.9762 and MIOU of 0.9025 for detecting and segmenting nodules on the JSRT dataset, which contains different sizes of nodules. Additionally, it achieves MIOU of 0.6579 and 0.7646 for small and medium size nodules, respectively. Keywords: Chest X-ray, Lung Cancer Segmentation, Computerized Tomography, Different Sizes of Nodules, Bone Suppression, Chest X-ray Enhancement and DRRs Generation.
546			_aText in English, abstracts in English and Arabic
650		4	_ainformatics
655		7	_2NULIB _aDissertation, Academic _9187
690			_ainformatics
942			_2ddc _cTH
999			_c10895 _d10895