PCA Based Dimensional Data Reduction and Segmentation for DICOM Images

Arulananth, T. S.; Balaji, L.; Baskar, M.; Anbarasu, V.; Rao, Koppula Srinivas

doi:10.1007/s11063-020-10391-9

Cited by 29 publications

(6 citation statements)

References 12 publications

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“…incorporation of a Cross-Layer Attention Module (CLAM). The edge detection based fast pixel matching and PCA based image segmentation is discussed in [34,35].…”

Section: Plos Onementioning

confidence: 99%

Semantic segmentation of urban environments: Leveraging U-Net deep learning model for cityscape image analysis

Arulananth,

Kuppusamy,

Ayyasamy

et al. 2024

PLoS ONE

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Semantic segmentation of cityscapes via deep learning is an essential and game-changing research topic that offers a more nuanced comprehension of urban landscapes. Deep learning techniques tackle urban complexity and diversity, which unlocks a broad range of applications. These include urban planning, transportation management, autonomous driving, and smart city efforts. Through rich context and insights, semantic segmentation helps decision-makers and stakeholders make educated decisions for sustainable and effective urban development. This study investigates an in-depth exploration of cityscape image segmentation using the U-Net deep learning model. The proposed U-Net architecture comprises an encoder and decoder structure. The encoder uses convolutional layers and down sampling to extract hierarchical information from input images. Each down sample step reduces spatial dimensions, and increases feature depth, aiding context acquisition. Batch normalization and dropout layers stabilize models and prevent overfitting during encoding. The decoder reconstructs higher-resolution feature maps using "UpSampling2D" layers. Through extensive experimentation and evaluation of the Cityscapes dataset, this study demonstrates the effectiveness of the U-Net model in achieving state-of-the-art results in image segmentation. The results clearly shown that, the proposed model has high accuracy, mean IOU and mean DICE compared to existing models.

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“…incorporation of a Cross-Layer Attention Module (CLAM). The edge detection based fast pixel matching and PCA based image segmentation is discussed in [34,35].…”

Section: Plos Onementioning

confidence: 99%

Semantic segmentation of urban environments: Leveraging U-Net deep learning model for cityscape image analysis

Arulananth,

Kuppusamy,

Ayyasamy

et al. 2024

PLoS ONE

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“…The authors [11] modeled a deep ensemble learning structure to harness DL techniques to tap the 'wisdom of experts' and compile multisource data. At the voting layer, two sparse AEs were instructed for feature learning to minimize the correlation of features and expand the base classifier eventually.…”

Section: Literature Reviewmentioning

confidence: 99%

Modeling of Whale Optimization with Deep Learning based Brain Disorder Detection and Classification

Uvaneshwari¹,

Baskar²

2023

IJACSA

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Brain disorders are a significant source of economic strain and unfathomable suffering in modern society. Imaging techniques help diagnose, monitor and treat mental health, neurological, and developmental disorders. To aid in the Computer-Aided Diagnosis of brain diseases, deep learning (DL) was used for the analysis of neuroimages from modalities including Positron Emission Tomography (PET), Structural Magnetic Resonance Imaging (SMRI), and functional MRI. In this study, a Whale Optimization Algorithm is used with Deep Learning to analyse MRI scans for signs of neurological disease. WOADL-BDDC may detect and label abnormalities in the brain based on an MRI scan. It uses a two-step pre-processing procedure, first using guided filtering to get rid of background noise and then using U-Net segmentation to get rid of the top of the head. QuickNAT, along with RMSProp, is used to segment the brain. When analysing data, WOADL-BDDC uses radionics to collect information from every layer. When used in a convolutional recurrent neural network model, the Whale Optimization Algorithm may accurately categorize mental illness. WOADL-BDDC is put through its paces using ADNI 3D. Compared to state-of-the-art classification results from Vgg16, Graph CNN, Modified ResNet18, Non-linear SVM, ResNet50-SVM, ResNet50-RF, the suggested technique achieved the greatest accuracy. It was demonstrated that the suggested model is superior to other models for classification from MRI images. In simulations, the proposed approach is shown to be effective in optimizing hyperparameters with an accuracy of 94.38 % on TR set and 94.87% on TS set, Precision of 96.43% on TR set and 97.62% on TS set, and an F1-Score of 89.35 % and 92.10% on TR and TS set, respectively.

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“…A PCA-based classification model is sketched in [20], which classifies the finger quickly. A face recognition model is sketched in [21], which considers contour features in classification.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning CNN Model-Based Anomaly Detection in 3D Brain MRI Images using Feature Distribution Similarity

Panyala¹,

Baskar²

2023

IJACSA

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Towards detecting an anomaly in brain images, different approaches are discussed in the literature. Features like white mass values and shape features have identified the presence of brain tumors. Various deep learning models like the neural network has been adapted to the problem tumor detection and suffers to meet maximum accuracy in detecting brain tumor. An Adaptive Feature Centric Distribution Similarity Based Anomaly Detection Model with Convolution Neural Network (AFCD-CNN) is sketched towards disease prediction problem to handle the problem. The model considers black-and-white mass features with the distribution of features. First, the method applies the Multi-Hop Neighbor Analysis (MHNA) algorithm in normalizing the brain image. Further, the process uses the Adaptive Mass Determined Segmentation (AMDS) algorithm, which groups the pixels of MRI according to the white and black mass values. The method extracts the ROI with the segmented image and convolves the features with CNN at the training phase. The CNN is designed to convolve the features into one dimension. The output layer neurons are designed to estimate different Feature Distribution Similarity (FDS) values against various features to compute the Anomaly Class Weight (ACW).According to the ACW value, anomaly detection is performed with higher accuracy up to 97% where the time complexity is reduced up to 32 seconds.

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PCA Based Dimensional Data Reduction and Segmentation for DICOM Images

Cited by 29 publications

References 12 publications

Semantic segmentation of urban environments: Leveraging U-Net deep learning model for cityscape image analysis

Semantic segmentation of urban environments: Leveraging U-Net deep learning model for cityscape image analysis

Modeling of Whale Optimization with Deep Learning based Brain Disorder Detection and Classification

Deep Learning CNN Model-Based Anomaly Detection in 3D Brain MRI Images using Feature Distribution Similarity

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