MyMedR

Displaying all 7 publications

Abstract:

Sort:

ExHiF: Alzheimer's disease detection using exemplar histogram-based features with CT and MR images

Kaplan E, Baygin M, Barua PD, Dogan S, Tuncer T, Altunisik E, et al.

Med Eng Phys, 2023 May;115:103971.
PMID: 37120169 DOI: 10.1016/j.medengphy.2023.103971

PURPOSE: The classification of medical images is an important priority for clinical research and helps to improve the diagnosis of various disorders. This work aims to classify the neuroradiological features of patients with Alzheimer's disease (AD) using an automatic hand-modeled method with high accuracy.
MATERIALS AND METHOD: This work uses two (private and public) datasets. The private dataset consists of 3807 magnetic resonance imaging (MRI) and computer tomography (CT) images belonging to two (normal and AD) classes. The second public (Kaggle AD) dataset contains 6400 MR images. The presented classification model comprises three fundamental phases: feature extraction using an exemplar hybrid feature extractor, neighborhood component analysis-based feature selection, and classification utilizing eight different classifiers. The novelty of this model is feature extraction. Vision transformers inspire this phase, and hence 16 exemplars are generated. Histogram-oriented gradients (HOG), local binary pattern (LBP) and local phase quantization (LPQ) feature extraction functions have been applied to each exemplar/patch and raw brain image. Finally, the created features are merged, and the best features are selected using neighborhood component analysis (NCA). These features are fed to eight classifiers to obtain highest classification performance using our proposed method. The presented image classification model uses exemplar histogram-based features; hence, it is called ExHiF.
RESULTS: We have developed the ExHiF model with a ten-fold cross-validation strategy using two (private and public) datasets with shallow classifiers. We have obtained 100% classification accuracy using cubic support vector machine (CSVM) and fine k nearest neighbor (FkNN) classifiers for both datasets.
CONCLUSIONS: Our developed model is ready to be validated with more datasets and has the potential to be employed in mental hospitals to assist neurologists in confirming their manual screening of AD using MRI/CT images.
Fulltext Swin-textural: A novel textural features-based image classification model for COVID-19 detection on chest computed tomography

Tuncer I, Barua PD, Dogan S, Baygin M, Tuncer T, Tan RS, et al.

Inform Med Unlocked, 2023;36:101158.
PMID: 36618887 DOI: 10.1016/j.imu.2022.101158

BACKGROUND: Chest computed tomography (CT) has a high sensitivity for detecting COVID-19 lung involvement and is widely used for diagnosis and disease monitoring. We proposed a new image classification model, swin-textural, that combined swin-based patch division with textual feature extraction for automated diagnosis of COVID-19 on chest CT images. The main objective of this work is to evaluate the performance of the swin architecture in feature engineering.
MATERIAL AND METHOD: We used a public dataset comprising 2167, 1247, and 757 (total 4171) transverse chest CT images belonging to 80, 80, and 50 (total 210) subjects with COVID-19, other non-COVID lung conditions, and normal lung findings. In our model, resized 420 × 420 input images were divided using uniform square patches of incremental dimensions, which yielded ten feature extraction layers. At each layer, local binary pattern and local phase quantization operations extracted textural features from individual patches as well as the undivided input image. Iterative neighborhood component analysis was used to select the most informative set of features to form ten selected feature vectors and also used to select the 11th vector from among the top selected feature vectors with accuracy >97.5%. The downstream kNN classifier calculated 11 prediction vectors. From these, iterative hard majority voting generated another nine voted prediction vectors. Finally, the best result among the twenty was determined using a greedy algorithm.
RESULTS: Swin-textural attained 98.71% three-class classification accuracy, outperforming published deep learning models trained on the same dataset. The model has linear time complexity.
CONCLUSIONS: Our handcrafted computationally lightweight swin-textural model can detect COVID-19 accurately on chest CT images with low misclassification rates. The model can be implemented in hospitals for efficient automated screening of COVID-19 on chest CT images. Moreover, findings demonstrate that our presented swin-textural is a self-organized, highly accurate, and lightweight image classification model and is better than the compared deep learning models for this dataset.
Automated Urine Cell Image Classification Model Using Chaotic Mixer Deep Feature Extraction

Erten M, Tuncer I, Barua PD, Yildirim K, Dogan S, Tuncer T, et al.

J Digit Imaging, 2023 Aug;36(4):1675-1686.
PMID: 37131063 DOI: 10.1007/s10278-023-00827-8

Microscopic examination of urinary sediments is a common laboratory procedure. Automated image-based classification of urinary sediments can reduce analysis time and costs. Inspired by cryptographic mixing protocols and computer vision, we developed an image classification model that combines a novel Arnold Cat Map (ACM)- and fixed-size patch-based mixer algorithm with transfer learning for deep feature extraction. Our study dataset comprised 6,687 urinary sediment images belonging to seven classes: Cast, Crystal, Epithelia, Epithelial nuclei, Erythrocyte, Leukocyte, and Mycete. The developed model consists of four layers: (1) an ACM-based mixer to generate mixed images from resized 224 × 224 input images using fixed-size 16 × 16 patches; (2) DenseNet201 pre-trained on ImageNet1K to extract 1,920 features from each raw input image, and its six corresponding mixed images were concatenated to form a final feature vector of length 13,440; (3) iterative neighborhood component analysis to select the most discriminative feature vector of optimal length 342, determined using a k-nearest neighbor (kNN)-based loss function calculator; and (4) shallow kNN-based classification with ten-fold cross-validation. Our model achieved 98.52% overall accuracy for seven-class classification, outperforming published models for urinary cell and sediment analysis. We demonstrated the feasibility and accuracy of deep feature engineering using an ACM-based mixer algorithm for image preprocessing combined with pre-trained DenseNet201 for feature extraction. The classification model was both demonstrably accurate and computationally lightweight, making it ready for implementation in real-world image-based urine sediment analysis applications.
PFP-HOG: Pyramid and Fixed-Size Patch-Based HOG Technique for Automated Brain Abnormality Classification with MRI

Kaplan E, Chan WY, Altinsoy HB, Baygin M, Barua PD, Chakraborty S, et al.

J Digit Imaging, 2023 Dec;36(6):2441-2460.
PMID: 37537514 DOI: 10.1007/s10278-023-00889-8

Detecting neurological abnormalities such as brain tumors and Alzheimer's disease (AD) using magnetic resonance imaging (MRI) images is an important research topic in the literature. Numerous machine learning models have been used to detect brain abnormalities accurately. This study addresses the problem of detecting neurological abnormalities in MRI. The motivation behind this problem lies in the need for accurate and efficient methods to assist neurologists in the diagnosis of these disorders. In addition, many deep learning techniques have been applied to MRI to develop accurate brain abnormality detection models, but these networks have high time complexity. Hence, a novel hand-modeled feature-based learning network is presented to reduce the time complexity and obtain high classification performance. The model proposed in this work uses a new feature generation architecture named pyramid and fixed-size patch (PFP). The main aim of the proposed PFP structure is to attain high classification performance using essential feature extractors with both multilevel and local features. Furthermore, the PFP feature extractor generates low- and high-level features using a handcrafted extractor. To obtain the high discriminative feature extraction ability of the PFP, we have used histogram-oriented gradients (HOG); hence, it is named PFP-HOG. Furthermore, the iterative Chi2 (IChi2) is utilized to choose the clinically significant features. Finally, the k-nearest neighbors (kNN) with tenfold cross-validation is used for automated classification. Four MRI neurological databases (AD dataset, brain tumor dataset 1, brain tumor dataset 2, and merged dataset) have been utilized to develop our model. PFP-HOG and IChi2-based models attained 100%, 94.98%, 98.19%, and 97.80% using the AD dataset, brain tumor dataset1, brain tumor dataset 2, and merged brain MRI dataset, respectively. These findings not only provide an accurate and robust classification of various neurological disorders using MRI but also hold the potential to assist neurologists in validating manual MRI brain abnormality screening.
Fulltext Multilevel Deep Feature Generation Framework for Automated Detection of Retinal Abnormalities Using OCT Images

Barua PD, Chan WY, Dogan S, Baygin M, Tuncer T, Ciaccio EJ, et al.

Entropy (Basel), 2021 Dec 08;23(12).
PMID: 34945957 DOI: 10.3390/e23121651

Optical coherence tomography (OCT) images coupled with many learning techniques have been developed to diagnose retinal disorders. This work aims to develop a novel framework for extracting deep features from 18 pre-trained convolutional neural networks (CNN) and to attain high performance using OCT images. In this work, we have developed a new framework for automated detection of retinal disorders using transfer learning. This model consists of three phases: deep fused and multilevel feature extraction, using 18 pre-trained networks and tent maximal pooling, feature selection with ReliefF, and classification using the optimized classifier. The novelty of this proposed framework is the feature generation using widely used CNNs and to select the most suitable features for classification. The extracted features using our proposed intelligent feature extractor are fed to iterative ReliefF (IRF) to automatically select the best feature vector. The quadratic support vector machine (QSVM) is utilized as a classifier in this work. We have developed our model using two public OCT image datasets, and they are named database 1 (DB1) and database 2 (DB2). The proposed framework can attain 97.40% and 100% classification accuracies using the two OCT datasets, DB1 and DB2, respectively. These results illustrate the success of our model.
Fulltext Automatic COVID-19 Detection Using Exemplar Hybrid Deep Features with X-ray Images

Barua PD, Muhammad Gowdh NF, Rahmat K, Ramli N, Ng WL, Chan WY, et al.

Int J Environ Res Public Health, 2021 07 29;18(15).
PMID: 34360343 DOI: 10.3390/ijerph18158052

COVID-19 and pneumonia detection using medical images is a topic of immense interest in medical and healthcare research. Various advanced medical imaging and machine learning techniques have been presented to detect these respiratory disorders accurately. In this work, we have proposed a novel COVID-19 detection system using an exemplar and hybrid fused deep feature generator with X-ray images. The proposed Exemplar COVID-19FclNet9 comprises three basic steps: exemplar deep feature generation, iterative feature selection and classification. The novelty of this work is the feature extraction using three pre-trained convolutional neural networks (CNNs) in the presented feature extraction phase. The common aspects of these pre-trained CNNs are that they have three fully connected layers, and these networks are AlexNet, VGG16 and VGG19. The fully connected layer of these networks is used to generate deep features using an exemplar structure, and a nine-feature generation method is obtained. The loss values of these feature extractors are computed, and the best three extractors are selected. The features of the top three fully connected features are merged. An iterative selector is used to select the most informative features. The chosen features are classified using a support vector machine (SVM) classifier. The proposed COVID-19FclNet9 applied nine deep feature extraction methods by using three deep networks together. The most appropriate deep feature generation model selection and iterative feature selection have been employed to utilise their advantages together. By using these techniques, the image classification ability of the used three deep networks has been improved. The presented model is developed using four X-ray image corpora (DB1, DB2, DB3 and DB4) with two, three and four classes. The proposed Exemplar COVID-19FclNet9 achieved a classification accuracy of 97.60%, 89.96%, 98.84% and 99.64% using the SVM classifier with 10-fold cross-validation for four datasets, respectively. Our developed Exemplar COVID-19FclNet9 model has achieved high classification accuracy for all four databases and may be deployed for clinical application.
Fulltext Automated detection of pain levels using deep feature extraction from shutter blinds-based dynamic-sized horizontal patches with facial images

Barua PD, Baygin N, Dogan S, Baygin M, Arunkumar N, Fujita H, et al.

Sci Rep, 2022 Oct 14;12(1):17297.
PMID: 36241674 DOI: 10.1038/s41598-022-21380-4

Pain intensity classification using facial images is a challenging problem in computer vision research. This work proposed a patch and transfer learning-based model to classify various pain intensities using facial images. The input facial images were segmented into dynamic-sized horizontal patches or "shutter blinds". A lightweight deep network DarkNet19 pre-trained on ImageNet1K was used to generate deep features from the shutter blinds and the undivided resized segmented input facial image. The most discriminative features were selected from these deep features using iterative neighborhood component analysis, which were then fed to a standard shallow fine k-nearest neighbor classifier for classification using tenfold cross-validation. The proposed shutter blinds-based model was trained and tested on datasets derived from two public databases-University of Northern British Columbia-McMaster Shoulder Pain Expression Archive Database and Denver Intensity of Spontaneous Facial Action Database-which both comprised four pain intensity classes that had been labeled by human experts using validated facial action coding system methodology. Our shutter blinds-based classification model attained more than 95% overall accuracy rates on both datasets. The excellent performance suggests that the automated pain intensity classification model can be deployed to assist doctors in the non-verbal detection of pain using facial images in various situations (e.g., non-communicative patients or during surgery). This system can facilitate timely detection and management of pain.