COMPUTER-AIDED MODEL FOR BREAST CANCER DETECTION IN MAMMOGRAMS

The objective of this research was to introduce a new system for automated detection of breast masses in mammography images. The system will be able to discriminate if the image has a mass or not, as well as benign and malignant masses. The new automated ROI segmentation model, where a profiling model integrated with a new iterative growing region scheme has been proposed. The ROI region segmentation is integrated with both statistical and texture feature extraction and selection to discriminate suspected regions effectively. A classifier model is designed using linear fisher classifier for suspected region identification. To check the system's performance, a large mammogram database has been used for experimental analysis. Sensitivity, specificity, and accuracy have been used as performance measures. In this study, the methods yielded an accuracy of 93% for normal/abnormal classification and a 79% accuracy for bening/malignant classification. The proposed model had an improvement of 8% for normal/abnormal classification, and a 7% improvement for benign/malignant classification over Naga et al., 2001. Moreover, the model improved 8% for normal/abnormal classification over Subashimi et al., 2015. The early diagnosis of this disease has a major role in its treatment. Thus the use of computer systems as a detection tool could be viewed as essential to helping with this disease

Intelligent Computer-Aided Model for Efficient Diagnosis of Digital Breast Tomosynthesis 3D Imaging Using Deep Learning

Abstract: Digital Breast Tomosynthesis (DBT) is a highly promising 3D imaging modality for breast diagnosis. Tissue overlapping is a challenge with traditional 2D mammograms, however since digital breast tomosynthesis can obtain three-dimensional images, tissue overlapping is reduced, making it easier for radiologists to detect abnormalities and resulting in improved and more accurate diagnosis. In this study, a new computer-aided multi-class diagnosis system is proposed that integrates DBT augmentation and colour feature map with a modified deep learn-ing architecture (Mod_AlexNet). In addition, an optimization layer is added with multiple opti-mizers for effective classification of multiple breast classes, including benign, normal, and ma-lignant. The proposed system comprises several techniques, including data augmentation, col-our feature mapping, optimization, and classification. Two experimental scenarios are applied, the first scenario proposed a computer-aided diagnosis (CAD) model that integrated DBT aug-mentation, image enhancement techniques and colour feature mapping with six deep learning models for feature extraction, including ResNet-18, AlexNet, GoogleNet, MobileNetV2, VGG-16 and DenseNet-201, to efficiently classify DBT slices. The second scenario compared the perfor-mance of the newly proposed Mod_AlexNet architecture and traditional AlexNet, using several optimization techniques and different evaluation performance metrics were computed. The op-timization techniques included Adaptive Moment Estimation (Adam), Root Mean Squared Prop-agation (RMSProp), and Stochastic Gradient Descent with Momentum (SGDM), for different batch sizes, including 32, 64 and 512. Experiments have been conducted on a large benchmark da-taset of breast tomography scans. The performance of the first scenario was compared in terms of accuracy, precision, sensitivity, specificity, runtime, and f1-score. While in the second scenar-io, performance was compared in terms of training accuracy, training loss, and test accuracy. In the first scenario, results demonstrated that AlexNet reported improvement rates of 1.69%, 5.13%, 6.13%, 4.79% and 1.6%, compared to ResNet-18, MobileNetV2, GoogleNet, DenseNet-201 and VGG16, respectively. Experimental analysis with different optimization techniques and batch sizes demonstrated that the proposed Mod_AlexNet architecture outperformed AlexNet in terms of test accuracy with an average improvement rate of 2.01%, 1.17% and 0.96% when com-pared using SGDM, Adam, and RMSProp optimizers, respectively

Analytical framework for Adaptive Compressive Sensing for Target Detection within Wireless Visual Sensor Networks

Wireless visual sensor networks (WVSNs) are composed of a large number of visual sensor nodes covering a specifc geographical region. This paper addresses the target detection problem within WVSNs where visual sensor nodes are left unattended for long-term deployment. As battery energy is a critical issue it is always challenging to maximize the network's lifetime. In order to reduce energy consumption, nodes undergo cycles of active-sleep periods that save their battery energy by switching sensor nodes ON and OFF, according to predefined duty cycles. Moreover, adaptive compressive sensing is expected to dynamically reduce the size of transmitted data through the wireless channel, saving communication bandwidth and consequently saving energy. This paper derives for the first time an analytical framework for selecting node's duty cycles and dynamically choosing the appropriate compression rates for the captured images and videos based on their sparsity nature. This reduces energy waste by reaching the maximum compression rate for each dataset without compromising the probability of detection. Experiments were conducted on different standard datasets resembling different scenes; indoor and outdoor, for single and multiple targets detection. Moreover, datasets were chosen with different sparsity levels to investigate the effect of sparsity on the compression rates. Results showed that by selecting duty cycles and dynamically choosing the appropriate compression rates, the desired performanc

Optimization of graph sub-structures using intelligent swarm agents

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

DETECT-LC: A 3D Deep Learning and Textural Radiomics Computational Model for Lung Cancer Staging and Tumor Phenotyping Based on Computed Tomography Volumes

Lung Cancer is one of the primary causes of cancer-related deaths worldwide. Timely diagnosis and precise staging are pivotal for treatment planning, and thus can lead to increased survival rates. The application of advanced machine learning techniques helps in effective diagnosis and staging. In this study, a multistage neurobased computational model is proposed, DETECT-LC learning. DETECT-LC handles the challenge of choosing discriminative CT slices for constructing 3D volumes, using Haralick, histogram-based radiomics, and unsupervised clustering. ALT-CNN-DENSE Net architecture is introduced as part of DETECT-LC for voxel-based classification. DETECT-LC offers an automatic threshold-based segmentation approach instead of the manual procedure, to help mitigate this burden for radiologists and clinicians. Also, DETECT-LC presents a slice selection approach and a newly proposed relatively light weight 3D CNN architecture to improve existing studies performance. The proposed pipeline is employed for tumor phenotyping and staging. DETECT-LC performance is assessed through a range of experiments, in which DETECT-LC attains outstanding performance surpassing its counterparts in terms of accuracy, sensitivity, F1-score and Area under Curve (AuC). For histopathology classification, DETECT-LC average performance achieved an improvement of 20% in overall accuracy, 0.19 in sensitivity, 0.16 in F1-Score and 0.16 in AuC over the state of the art. A similar enhancement is reached for staging, where higher overall accuracy, sensitivity and F1-score are attained with differences of 8%, 0.08 and 0.14

<i>DETECT-LC</i>: A 3D Deep Learning and Textural Radiomics Computational Model for Lung Cancer Staging and Tumor Phenotyping Based on Computed Tomography Volumes

Lung Cancer is one of the primary causes of cancer-related deaths worldwide. Timely diagnosis and precise staging are pivotal for treatment planning, and thus can lead to increased survival rates. The application of advanced machine learning techniques helps in effective diagnosis and staging. In this study, a multistage neurobased computational model is proposed, DETECT-LC learning. DETECT-LC handles the challenge of choosing discriminative CT slices for constructing 3D volumes, using Haralick, histogram-based radiomics, and unsupervised clustering. ALT-CNN-DENSE Net architecture is introduced as part of DETECT-LC for voxel-based classification. DETECT-LC offers an automatic threshold-based segmentation approach instead of the manual procedure, to help mitigate this burden for radiologists and clinicians. Also, DETECT-LC presents a slice selection approach and a newly proposed relatively light weight 3D CNN architecture to improve existing studies performance. The proposed pipeline is employed for tumor phenotyping and staging. DETECT-LC performance is assessed through a range of experiments, in which DETECT-LC attains outstanding performance surpassing its counterparts in terms of accuracy, sensitivity, F1-score and Area under Curve (AuC). For histopathology classification, DETECT-LC average performance achieved an improvement of 20% in overall accuracy, 0.19 in sensitivity, 0.16 in F1-Score and 0.16 in AuC over the state of the art. A similar enhancement is reached for staging, where higher overall accuracy, sensitivity and F1-score are attained with differences of 8%, 0.08 and 0.14

Dynamically Adaptive Data Clustering Using Intelligent Swarm-like Agents

Abstract ⎯ Inspired by the self-organized behaviour of bird flocks, a new dynamic clustering approach based on Particle Swarm Optimization is proposed. This paper introduces a novel clustering method, the PSDC, a new Particle Swarm-like agents approach for Dynamically Adaptive data clustering. Unlike other partition clustering algorithms, this technique does not require initial partitioned seeds and it can dynamically adapt to the changes in the global shape or size of the clusters. In this technique, the agents have lots of useful features such as sensing, thinking, making decisions, parallelism and moving freely in the solution space. The moving swarm-like agents are guided to move according to a specific proposed navigation rules. These rules help every agent to find its new position in its navigation process and the clustering results emerge from the collective and cooperative behaviour of these swarm agents. If the swarm performance showed gradual improvements during a predefined number of cycles, then the current population could pass useful information to the next population in order to help further generations in reaching better solutions faster and enable the learning process to be reinforced. The distributed, adaptive and cooperative behaviour of these agents was so powerful to explore the solution space effectively. Through the cooperative behaviour, the generations of agents were able to build knowledge and the whole population could pass information to the next generation. Numerous experiments have been conducted using both synthetic and real datasets to evaluate the efficiency of the proposed model. Cluster validity approaches are used to quantitatively evaluate the results of the clustering algorithm. Experimental results showed that the proposed particle swarm-like clustering algorithm reaches good clustering solutions and achieves superior performance compared to others

HyCAD-OCT: A Hybrid Computer-Aided Diagnosis of Retinopathy by Optical Coherence Tomography Integrating Machine Learning and Feature Maps Localization

Optical Coherence Tomography (OCT) imaging has major advantages in effectively identifying the presence of various ocular pathologies and detecting a wide range of macular diseases. OCT examinations can aid in the detection of many retina disorders in early stages that could not be detected in traditional retina images. In this paper, a new hybrid computer-aided OCT diagnostic system (HyCAD) is proposed for classification of Diabetic Macular Edema (DME), Choroidal Neovascularization (CNV) and drusen disorders, while separating them from Normal OCT images. The proposed HyCAD hybrid learning system integrates the segmentation of Region of Interest (RoI), based on central serious chorioretinopathy (CSC) in Spectral Domain Optical Coherence Tomography (SD-OCT) images, with deep learning architectures for effective diagnosis of retinal disorders. The proposed system assimilates a range of techniques including RoI localization and feature extraction, followed by classification and diagnosis. An efficient feature fusion phase has been introduced for combining the OCT image features, extracted by Deep Convolutional Neural Network (CNN), with the features extracted from the RoI segmentation phase. This fused feature set is used to predict multiclass OCT retina disorders. The proposed segmentation phase of retinal RoI regions adds substantial contribution as it draws attention to the most significant areas that are candidate for diagnosis. A new modified deep learning architecture (Norm-VGG16) is introduced integrating a kernel regularizer. Norm-VGG16 is trained from scratch on a large benchmark dataset and used in RoI localization and segmentation. Various experiments have been carried out to illustrate the performance of the proposed system. Large Dataset of Labeled Optical Coherence Tomography (OCT) v3 benchmark is used to validate the efficiency of the model compared with others in literature. The experimental results show that the proposed model achieves relatively high-performance in terms of accuracy, sensitivity and specificity. An average accuracy, sensitivity and specificity of 98.8%, 99.4% and 98.2% is achieved, respectively. The remarkable performance achieved reflects that the fusion phase can effectively improve the identification ratio of the urgent patients&rsquo; diagnostic images and clinical data. In addition, an outstanding performance is achieved compared to others in literature

A Hybrid Adaptive Compressive Sensing Model for Visual Tracking in Wireless Visual Sensor Networks

The employ of Wireless Visual Sensor Networks (WVSNs) has grown enormously in the last few years and have emerged in distinctive applications. WVSNs-based Surveillance applications are one of the important applications that requires high detection reliability and robust tracking, while minimizing the usage of energy to maximize the lifetime of sensor nodes as visual sensor nodes can be left for months without any human interaction. The constraints of WVSNs such as resource constraints due to limited battery power, memory space and communication bandwidth have brought new WVSNs implementation challenges. Hence, the aim of this paper is to investigate the impact of adaptive compressive Sensing (CS) in designing efficient target detection and tracking techniques, to reduce the size of transmitted data without compromising the tracking performance as well as space and energy constraints. In this paper, a new hybrid adaptive compressive sensing scheme is introduced to dynamically achieve higher compression rates, as different datasets have different sparsity nature that affects the compression. Afterwards, a modified quantized clipped Least Mean square (LMS) adaptive filter is proposed for the tracking model. Experimental results showed that adaptive CS achieved high compression rates reaching 70%, while preserving the detection and tracking accuracy which is measured in terms of mean squared error, peak-signal-to-noise-ratio and tracking trajectory