Pearson Correlation-Based Feature Selection for Document Classification Using Balanced Training.

Documents are stored in a digital form across several organizations. Printing this amount of data and placing it into folders instead of storing digitally is against the practical, economical, and ecological perspective. An efficient way of retrieving data from digitally stored documents is also required. This article presents a real-time supervised learning technique for document classification based on deep convolutional neural network (DCNN), which aims to reduce the impact of adverse document image issues such as signatures, marks, logo, and handwritten notes. The proposed technique's major steps include data augmentation, feature extraction using pre-trained neural network models, feature fusion, and feature selection. We propose a novel data augmentation technique, which normalizes the imbalanced dataset using the secondary dataset RVL-CDIP. The DCNN features are extracted using the VGG19 and AlexNet networks. The extracted features are fused, and the fused feature vector is optimized by applying a Pearson correlation coefficient-based technique to select the optimized features while removing the redundant features. The proposed technique is tested on the Tobacco3482 dataset, which gives a classification accuracy of 93.1% using a cubic support vector machine classifier, proving the validity of the proposed technique.

Stomach Deformities Recognition Using Rank-Based Deep Features Selection.

Doctor utilizes various kinds of clinical technologies like MRI, endoscopy, CT scan, etc., to identify patient's deformity during the review time. Among set of clinical technologies, wireless capsule endoscopy (WCE) is an advanced procedures used for digestive track malformation. During this complete process, more than 57,000 frames are captured and doctors need to examine a complete video frame by frame which is a tedious task even for an experienced gastrologist. In this article, a novel computerized automated method is proposed for the classification of abdominal infections of gastrointestinal track from WCE images. Three core steps of the suggested system belong to the category of segmentation, deep features extraction and fusion followed by robust features selection. The ulcer abnormalities from WCE videos are initially extracted through a proposed color features based low level and high-level saliency (CFbLHS) estimation method. Later, DenseNet CNN model is utilized and through transfer learning (TL) features are computed prior to feature optimization using Kapur's entropy. A parallel fusion methodology is opted for the selection of maximum feature value (PMFV). For feature selection, Tsallis entropy is calculated later sorted into descending order. Finally, top 50% high ranked features are selected for classification using multilayered feedforward neural network classifier for recognition. Simulation is performed on collected WCE dataset and achieved maximum accuracy of 99.5% in 21.15 s.

A New Approach for Brain Tumor Segmentation and Classification Based on Score Level Fusion Using Transfer Learning.

Brain tumor is one of the most death defying diseases nowadays. The tumor contains a cluster of abnormal cells grouped around the inner portion of human brain. It affects the brain by squeezing/ damaging healthy tissues. It also amplifies intra cranial pressure and as a result tumor cells growth increases rapidly which may lead to death. It is, therefore desirable to diagnose/ detect brain tumor at an early stage that may increase the patient survival rate. The major objective of this research work is to present a new technique for the detection of tumor. The proposed architecture accurately segments and classifies the benign and malignant tumor cases. Different spatial domain methods are applied to enhance and accurately segment the input images. Moreover Alex and Google networks are utilized for classification in which two score vectors are obtained after the softmax layer. Further, both score vectors are fused and supplied to multiple classifiers along with softmax layer. Evaluation of proposed model is done on top medical image computing and computer-assisted intervention (MICCAI) challenge datasets i.e., multimodal brain tumor segmentation (BRATS) 2013, 2014, 2015, 2016 and ischemic stroke lesion segmentation (ISLES) 2018 respectively.

Localization and grading of NPDR lesions using ResNet-18-YOLOv8 model and informative features selection for DR classification based on transfer learning.

Complications in diabetes lead to diabetic retinopathy (DR) hence affecting the vision. Computerized methods performed a significant role in DR detection at the initial phase to cure vision loss. Therefore, a method is proposed in this study that consists of three models for localization, segmentation, and classification. A novel technique is designed with the combination of pre-trained ResNet-18 and YOLOv8 models based on the selection of optimum layers for the localization of DR lesions. The localized images are passed to the designed semantic segmentation model on selected layers and trained on optimized learning hyperparameters. The segmentation model performance is evaluated on the Grand-challenge IDRID segmentation dataset. The achieved results are computed in terms of mean IoU 0.95,0.94, 0.96, 0.94, and 0.95 on OD, SoftExs, HardExs, HAE, and MAs respectively. Another classification model is developed in which deep features are derived from the pre-trained Efficientnet-b0 model and optimized using a Genetic algorithm (GA) based on the selected parameters for grading of NPDR lesions. The proposed model achieved greater than 98 % accuracy which is superior to previous methods.

Generative adversarial networks and its applications in the biomedical image segmentation: a comprehensive survey.

Recent advancements with deep generative models have proven significant potential in the task of image synthesis, detection, segmentation, and classification. Segmenting the medical images is considered a primary challenge in the biomedical imaging field. There have been various GANs-based models proposed in the literature to resolve medical segmentation challenges. Our research outcome has identified 151 papers; after the twofold screening, 138 papers are selected for the final survey. A comprehensive survey is conducted on GANs network application to medical image segmentation, primarily focused on various GANs-based models, performance metrics, loss function, datasets, augmentation methods, paper implementation, and source codes. Secondly, this paper provides a detailed overview of GANs network application in different human diseases segmentation. We conclude our research with critical discussion, limitations of GANs, and suggestions for future directions. We hope this survey is beneficial and increases awareness of GANs network implementations for biomedical image segmentation tasks.

Discrete light sheet microscopic segmentation of left ventricle using morphological tuning and active contours.

Left ventricular segmentation using cardiovascular MR scan is required for the diagnosis and further cure of cardiac diseases. Automatic systems for left ventricle segmentation are being studied for attaining more accurate results in a shorter period of time. A novel algorithm introducing discrete segmentation of left ventricle achieves an independent processing of images swiftly. The workflow consists of four segments; first, automated localization is performed on the MR image. Second, performing preprocessing intimately improves and enhances the quality of image using mean contrast adjustment. Central segmentation of endocardium and epicardium layers includes novel MTAC (Morphological tuning using active contours) segmentation algorithm that provides a perfect combination of active contours and morphological tuning to bring an adequate and desirable segmentation. The prospective snake model is a restrained progression, which takes iterations for an impulse throughout the left ventricle contours. At the end, contrast based refining overcomes minor edge problems for both outer and inner boundaries. Proposed algorithm is evaluated via Sunnybrook cardiac MR images by producing an overall average perpendicular distance 2.45 mm, an average dice matrix (endo: 91.3%; epi: 92.16%) and 91.7% dice matrix of overall endocardium and epicardium contours from ground truth contours.

Recognition of Knee Osteoarthritis (KOA) Using YOLOv2 and Classification Based on Convolutional Neural Network.

Knee osteoarthritis (KOA) is one of the deadliest forms of arthritis. If not treated at an early stage, it may lead to knee replacement. That is why early diagnosis of KOA is necessary for better treatment. Manually KOA detection is a time-consuming and error-prone task. Computerized methods play a vital role in accurate and speedy detection. Therefore, the classification and localization of the KOA method are proposed in this work using radiographic images. The two-dimensional radiograph images are converted into three-dimensional and LBP features are extracted having the dimension of N × 59 out of which the best features of N × 55 are selected using PCA. The deep features are also extracted using Alex-Net and Dark-net-53 with the dimensions of N × 1024 and N × 4096, respectively, where N represents the number of images. Then, N × 1000 features are selected individually from both models using PCA. Finally, the extracted features are fused serially with the dimension of N × 2055 and passed to the classifiers on a 10-fold cross-validation that provides an accuracy of 90.6% for the classification of KOA grades. The localization model is proposed with the combination of an open exchange neural network (ONNX) and YOLOv2 that is trained on the selected hyper-parameters. The proposed model provides 0.98 mAP for the localization of classified images. The experimental analysis proves that the presented framework provides better results as compared to existing works.

Detection and Classification of Gastrointestinal Diseases using Machine Learning.

BACKGROUND: Traditional endoscopy is an invasive and painful method of examining the gastrointestinal tract (GIT) not supported by physicians and patients. To handle this issue, video endoscopy (VE) or wireless capsule endoscopy (WCE) is recommended and utilized for GIT examination. Furthermore, manual assessment of captured images is not possible for an expert physician because it's a time taking task to analyze thousands of images thoroughly. Hence, there comes the need for a Computer-Aided-Diagnosis (CAD) method to help doctors analyze images. Many researchers have proposed techniques for automated recognition and classification of abnormality in captured images. METHODS: In this article, existing methods for automated classification, segmentation and detection of several GI diseases are discussed. Paper gives a comprehensive detail about these state-of-theart methods. Furthermore, literature is divided into several subsections based on preprocessing techniques, segmentation techniques, handcrafted features based techniques and deep learning based techniques. Finally, issues, challenges and limitations are also undertaken. RESULTS: A comparative analysis of different approaches for the detection and classification of GI infections. CONCLUSION: This comprehensive review article combines information related to a number of GI diseases diagnosis methods at one place. This article will facilitate the researchers to develop new algorithms and approaches for early detection of GI diseases detection with more promising results as compared to the existing ones of literature.

Classification of stomach infections: A paradigm of convolutional neural network along with classical features fusion and selection.

Automated detection and classification of gastric infections (i.e., ulcer, polyp, esophagitis, and bleeding) through wireless capsule endoscopy (WCE) is still a key challenge. Doctors can identify these endoscopic diseases by using the computer-aided diagnostic (CAD) systems. In this article, a new fully automated system is proposed for the recognition of gastric infections through multi-type features extraction, fusion, and robust features selection. Five key steps are performed-database creation, handcrafted and convolutional neural network (CNN) deep features extraction, a fusion of extracted features, selection of best features using a genetic algorithm (GA), and recognition. In the features extraction step, discrete cosine transform, discrete wavelet transform strong color feature, and VGG16-based CNN features are extracted. Later, these features are fused by simple array concatenation and GA is performed through which best features are selected based on K-Nearest Neighbor fitness function. In the last, best selected features are provided to Ensemble classifier for recognition of gastric diseases. A database is prepared using four datasets-Kvasir, CVC-ClinicDB, Private, and ETIS-LaribPolypDB with four types of gastric infections such as ulcer, polyp, esophagitis, and bleeding. Using this database, proposed technique performs better as compared to existing methods and achieves an accuracy of 96.5%.

Multistage segmentation model and SVM-ensemble for precise lung nodule detection.

PURPOSE: Lung cancer detection at its initial stages increases the survival chances of patients. Automatic detection of lung nodules facilitates radiologists during the diagnosis. However, there is a challenge of false positives in automated systems which may lead to wrong findings. Precise segmentation facilitates to accurately extract nodules from lung CT images in order to improve performance of the diagnostic method. METHODS: A multistage segmentation model is presented in this study. The lung region is extracted by applying corner-seeded region growing combined with differential evolution-based optimal thresholding. In addition to this, morphological operations are applied in boundary smoothing, hole filling and juxtavascular nodule extraction. Geometric properties along with 3D edge information are applied to extract nodule candidates. Geometric texture features descriptor (GTFD) followed by support vector machine-based ensemble classification is employed to distinguish actual nodules from the candidate set. RESULTS: A publicly available dataset, namely lung image database consortium and image database resource initiative, is used to evaluate performance of the proposed method. The classification is performed over GTFD feature vector and the results show 99% accuracy, 98.6% sensitivity and 98.2% specificity with 3.4 false positives per scan (FPs/scan). CONCLUSION: A lung nodule detection method is presented to facilitate radiologists in accurately diagnosing cancer from CT images. Results indicate that the proposed method has not only reduced FPs/scan but also significantly improved sensitivity as compared to related studies.

Computer assisted gastric abnormalities detection using hybrid texture descriptors for chromoendoscopy images.

BACKGROUND AND OBJECTIVE: The early diagnosis of stomach cancer can be performed by using a proper screening procedure. Chromoendoscopy (CH) is an image-enhanced video endoscopy technique, which is used for inspection of the gastrointestinal-tract by spraying dyes to highlight the gastric mucosal structures. An endoscopy session can end up with generating a large number of video frames. Therefore, inspection of every individual endoscopic-frame is an exhaustive task for the medical experts. In contrast with manual inspection, the automated analysis of gastroenterology images using computer vision based techniques can provide assistance to endoscopist, by finding out abnormal frames from the whole endoscopic sequence. METHODS: In this paper, we have presented a new feature extraction method named as Gabor-based gray-level co-occurrence matrix (G2LCM) for computer-aided detection of CH abnormal frames. It is a hybrid texture extraction approach which extracts a combination both local and global texture descriptors. Moreover, texture information of a CH image is represented by computing the gray level co-occurrence matrix of Gabor filters responses. Furthermore, the second-order statistics of these co-occurrence matrices are computed to represent images' texture. RESULTS: The obtained results show the possibility to correctly classifying abnormal from normal frames, with sensitivity, specificity, accuracy, and area under the curve as 91%, 82%, 87% and 0.91 respectively, by using a support vector machine classifier and G2LCM texture features. CONCLUSION: It is apparent from results that the proposed system can be used for providing aid to the gastroenterologist in the screening of the gastric tract. Ultimately, the time taken by an endoscopic procedure will be sufficiently reduced.

Fundus image classification methods for the detection of glaucoma: A review.

Glaucoma is a neurodegenerative illness and is considered as a standout amongst the most widely recognized reasons for visual impairment. Nerve's degeneration is an irretrievable procedure, so the diagnosis of the illness at an early stage is an absolute requirement to stay away from lasting loss of vision. Glaucoma effected mainly because of increased intraocular pressure, if it is not distinguished and looked early, it can result in visual impairment. There are not generally evident side effects of glaucoma; thus, patients attempt to get treatment just when the seriousness of malady is advanced altogether. Determination of glaucoma often comprises of review of the basic crumbling of the nerve in conjunction with the examination of visual function capacity. This article shows the persistent illustration of glaucoma, its side effects, and the potential people inclined to this malady. The essence of this article is on different classification methods being utilized and proposed by various scientists for the identification of glaucoma. This article audits a few division and segmentation methodologies that are exceptionally useful for recognizable proof, identification, and diagnosis of glaucoma. The research related to the findings and the treatment is likewise evaluated in this article.

Computer-based classification of chromoendoscopy images using homogeneous texture descriptors.

Computer-aided analysis of clinical pathologies is a challenging task in the field of medical imaging. Specifically, the detection of abnormal regions in the frames collected during an endoscopic session is difficult. The variations in the conditions of image acquisition, such as field of view or illumination modification, make it more demanding. Therefore, the design of a computer-assisted diagnostic system for the recognition of gastric abnormalities requires features that are robust to scale, rotation, and illumination variations of the images. Therefore, this study focuses on designing a set of texture descriptors based on the Gabor wavelets that will cope with certain image dynamics. The proposed features are extracted from the images and utilized for the classification of the chromoendoscopy (CH) frames into normal and abnormal categories. Moreover, to attain a higher accuracy, an optimized subset of descriptors is selected through the genetic algorithm. The results obtained using the proposed features are compared with other existing texture descriptors (e.g., local binary pattern and homogeneous texture descriptors). Furthermore, the selected features are used to train the support vector machine (SVM), naive Bayes (NB) algorithm, k-nearest neighbor algorithm, linear discriminant analysis, and ensemble tree classifier. The performance of these state-of-the-art classifiers for different texture descriptors is compared based on the accuracy, sensitivity, specificity, and area under the curve (AUC) derived by using the CH images. The classification results reveal that the SVM classifier achieves 90.0% average accuracy and 0.93 AUC when it is employed with an optimized set of features obtained by using a genetic algorithm.

A Review on Recent Developments for Detection of Diabetic Retinopathy.

Diabetic retinopathy is caused by the retinal micro vasculature which may be formed as a result of diabetes mellitus. Blindness may appear as a result of unchecked and severe cases of diabetic retinopathy. Manual inspection of fundus images to check morphological changes in microaneurysms, exudates, blood vessels, hemorrhages, and macula is a very time-consuming and tedious work. It can be made easily with the help of computer-aided system and intervariability for the observer. In this paper, several techniques for detecting microaneurysms, hemorrhages, and exudates are discussed for ultimate detection of nonproliferative diabetic retinopathy. Blood vessels detection techniques are also discussed for the diagnosis of proliferative diabetic retinopathy. Furthermore, the paper elaborates a discussion on the experiments accessed by authors for the detection of diabetic retinopathy. This work will be helpful for the researchers and technical persons who want to utilize the ongoing research in this area.