PAWS-net: prior-aware weakly supervised network for pulmonary disease localization and classification in CT scans

The contribution of a plant is highly important for both human life and environment. Plants do suffer from diseases, like human beings and animals. There is the number of plant diseases that occur and affects the normal growth of a plant. These diseases affect complete plant including leaf, stem, fruit, root, and flower. Most of the time when the disease of a plant has not been taken care of, the plant dies or may cause leaves drop, flowers, and fruits drop. Appropriate diagnosis of such diseases is required for accurate identification and treatment of plant diseases. Plant pathology is the study of plant diseases, their causes, procedures for controlling and managing them. But, the existing method encompasses human involvement for classification and identification of diseases. This procedure is time-consuming and costly. Automatic segmentation of diseases from plant leaf images using soft computing approach can be reasonably useful than the existing one. In this paper, we have introduced a method named as bacterial foraging optimization based radial basis function neural network (BRBFNN) for identification and classification of plant leaf diseases automatically. For assigning optimal weight to radial basis function neural network we use bacterial foraging optimization that further increases the speed and accuracy of the network to identify and classify the regions infected of different diseases on the plant leafs. The region growing algorithm increases the efficiency of the network by searching and grouping of seed points having common attributes for feature extraction process. We worked on fungal diseases like common rust, cedar apple rust, late blight, leaf curl, leaf spot, and early blight. The proposed method attains higher accuracy in identification and classification of diseases.

Identifying plant disease and severity from leaves: A deep multitask learning framework using triple-branch Swin Transformer and deep supervision

The role of imaging modalities in the diagnosis, differential diagnosis and clinical assessment of peripheral joint osteoarthritis

COVID-19 has been rapidly spreading worldwide and infected more than 1 million people with over 690k deaths reported. It is urgent and crucial to identify COVID-19-infected patients by computed tomography (CT) accurately and rapidly. However, we found that two problems, weak supervision and lack of interpretability, hindered its development. To address these challenges, we propose an attention-based multi-flow network for COVID-19 classification and lesion localization from chest CT. In the proposed model, we built a Resnet-based multi-flow network to learn the local information and the longitudinal information from the full chest sequence slice. To assist doctors in decision-making, the attention mechanism integrated into the network, which can locate the key slices and key parts from a full chest CT sequence of patients. We have systematically evaluated our method on the CT images of 1031 cases, including 420 COVID-19 cases, 311CAP cases, and 300 non-pneumonia cases. Our method could obtain an average accuracy of 82.3%, with 85.7% sensitivity and 86.4 % specificity, which outperformed previous works.

Accurate identification and localization of the vertebrae in CT scans is a critical and standard pre-processing step for clinical spinal diagnosis and treatment. Existing methods are mainly based on the integration of multiple neural networks, and most of them use heatmaps to locate the vertebrae's centroid. However, the process of obtaining vertebrae's centroid coordinates using heatmaps is non-differentiable, so it is impossible to train the network to label the vertebrae directly. Therefore, for end-to-end differential training of vertebrae coordinates on CT scans, a robust and accurate automatic vertebral labeling algorithm is proposed in this study. First, a novel end-to-end integral regression localization and multi-label classification network is developed, which can capture multi-scale features and also utilize the residual module and skip connection to fuse the multi-level features. Second, to solve the problem that the process of finding coordinates is non-differentiable and the spatial structure of location being destroyed, an integral regression module is used in the localization network. It combines the advantages of heatmaps representation and direct regression coordinates to achieve end-to-end training and can be compatible with any key point detection methods of medical images based on heatmaps. Finally, multi-label classification of vertebrae is carried out to improve the identification rate, which uses bidirectional long short-term memory (Bi-LSTM) online to enhance the learning of long contextual information of vertebrae. The proposed method is evaluated on a challenging data set, and the results are significantly better than state-of-the-art methods (identification rate is 91.1% and the mean localization error is 2.2 mm). The method is evaluated on a new CT data set, and the results show that our method has good generalization.

Computer-based identification of abnormal regions and classification of diseases using CT images of the lung has been a goal of many investigators. In this paper, we review research that has used texture analysis along with segmentation and fractal analysis. First, a review of texture methods is performed. Recent research on quantitative analysis of the lung using texture methods is categorized into six groups of computational methods: structural, statistical, model based, transform domain, texture-segmentation, and texture-fractal analysis. Finally, the applications of texture-based methods combined with either segmentation algorithms or fractal analysis is evaluated on lung CT images from patients with diseases such as emphysema, COPD, and cancer. We also discuss applications of artificial neural networks, support vector machine, k-nearest, and Bayesian methods to classify normal and diseased segments of CT images of the lung. A combination of these texture methods followed by classifiers could lead to efficient and accurate diagnosis of pulmonary diseases such as pulmonary fibrosis, emphysema, and cancer.

Deep learning method for localization and segmentation of abdominal CT

PREFACE: A SPECIAL SELECTION ON EMERGING TECHNIQUES FOR BIOMECHANICS–PART II

The accurate classification of gastrointestinal diseases from endoscopic images is essential for early detection and treatment. However, current methods face challenges in effectively integrating both global and local features, which limits their ability to capture both broad semantic information and subtle lesion details, ultimately affecting classification performance. To address this issue, this study introduces a novel deep learning framework, the Global and Local Interaction Network (GLI-Net). The GLI-Net consists of four main components: a Global Branch Module (GB) designed to extract global image features, a Local Branch Module (LB) focused on capturing detailed lesion features, an Information Exchange Module (LEM) that facilitates bidirectional information exchange and fusion between the global and local features, and an Adaptive Feature Fusion and Enhancement Module (AFE) aimed at optimizing the fused features. By integrating these modules, GLI-Net effectively captures and combines multi-level feature information, which improves both the accuracy and robustness of endoscopic image classification. Experiments conducted using the Kvasir and Hyper-Kvasir public datasets demonstrate that GLI-Net outperforms existing state-of-the-art models across several metrics, including accuracy, F1 score, precision, and recall. Additionally, ablation studies confirm the contribution of each module to the overall system performance. In summary, GLI-Net’s advanced feature extraction and fusion techniques significantly enhance medical endoscopic image classification, highlighting its potential for use in complex medical image analysis tasks.

Is Thoracic CT Performed Often Enough?

Spinal cord is a cylindrical shape located in Central Nervous System (CNS) and extended between the medulla oblongata and lumbar vertebrae. The main task in the classification of multilevel spinal cord disease in Computed Tomography (CT) is the accuracy. In this research, a Taylor Crow Search-Rider Optimization Algorithm (TaylorCSROA) is developed for accurate classification of spinal cord disease. The segmentation is done through the adaptive thresholding method. The Sparse Fuzzy C-Means clustering (Sparse FCM) algorithm is implemented for the localization of disc. The features, like connectivity, Local Optimal-Oriented Pattern (LOOP), statistical, image-level, Grid-based shape features, and tetrolet features are extracted. The spinal cord disease is classified into intervertebral disc (IVD) bulges, the cal sac compressing, bone marrow disease, central or foraminal stenosis, annular tears, scoliosis, end plate degeneration, endplate defects (modic type), facet connection, and ligamentum flavum hypertrophy, and spondylolisthes is by training the Deep Residual Network using the developed TaylorCSROA. The developed TaylorCSROA algorithm derived from the integration of Taylor Series, Crow Search Algorithm (CSA), and the Rider Optimization Algorithm (ROA). When compared with the existing spinal cord disease classification methods, the developed method obtained a maximum accuracy of 0.9102, sensitivity of 0.8945, and specificity of 0.9245.

PCAN: Pixel-wise classification and attention network for thoracic disease classification and weakly supervised localization.

Accurate classification and precise quantification of interstitial lung disease (ILD) types on CT images remain important challenges in clinical diagnosis. Multi-modality image information is required to assist diagnosing diseases. To build scalable deep-learning solutions for this problem, how to take full advantage of existing large-scale datasets in modern hospitals has become a critical task. In this paper, we present DeepILD, as a novel computer-aided diagnostic framework to address the ILD classification task only from single modality (CT image) using a deep neural network. More specifically, we propose integrating spherical semi-supervised K- means clustering and convolutional neural networks for ILD classification and disease quantification. We firstly use semi-supervised spherical K-means to divide the CT lung area into normal and abnormal sub-regions. A convolutional neural network (CNN) is subsequently invoked to perform training using image patches extracted from the abnormal regions. Here, we focus on the classification of three chronic fibrosing ILD types: idiopathic pulmonary fibrosis (IPF), idiopathic non-specific interstitial pneumonia (iNSIP), and chronic hypersensitivity pneumonia (CHP). Excellent classification accuracy has been achieved using a dataset of 188 CT scans; in particular, our IPF classification reached about 88% accuracy.

The proposed research work presents image segmentation of cardiac MRI Images of ventricle segmentation. Most important task in image analysis is Segmentation of Images. Data Mining and Machine Learning approaches are now a day very much used for Left Ventricle Segmentation on Cardiac System which efficiently uses different kind of algorithms. There are various measuring tools to evaluate the chest pain, cardiac function, neurologic deficits, by Cardiac System. Today the cardiac related diseases are increasing too much in our society. So, earlier identification of disease is crucial for urgent treatment of cardiac diseases. Doctors are providing their suggestion on the basis of manual inspection with MRI and CT scans. Here, the task is based on technical work with morphological, threshold based segmentation and fuzzy based edge detection approach is applied for better classification of diseases. The task is used to classify cardiac arrhythmia cases, abnormal cardiac cases, left ventricular cases problems. So specifically medical based image segmentation consists higher impotency which betterment the organs localizations for betterment of the quality of the diagnosis and crucial works and become crucial stages for evaluation of functionality of heart failure with pre existing approaches. Here, the the performance produces by this method is more than 90% of accuracy for detection and classification of cardiac diseases. Some of the statistical parameter are basically MSE, PSNR, MAE etc are significantly performs better for the fuzzy based approach with better quality performance of MR images.

Many types of deep neural networks have been proposed to address the problem of human biometric identification, especially in the areas of face detection and recognition. Local deep neural networks have been recently used in face-based age and gender classification, despite their improvement in performance, their costs on model training is rather expensive. In this paper, we propose to construct a local deep neural network for age and gender classification. In our proposed model, local image patches are selected based on the detected facial landmarks; the selected patches are then used for the network training. A holistical edge map for an entire image is also used for training a “global” network. The age and gender classification results are obtained by combining both the outputs from both the “global” and the local networks. Our proposed model is tested on two face image benchmark datasets; competitive performance is obtained compared to the state-of-the-art methods.