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Detecting lung diseases in medical images can be quite challenging for radiologists. In some cases, even experienced experts may struggle with accurately diagnosing chest diseases, leading to potential inaccuracies due to complex or unseen biomarkers. This review paper delves into various datasets and machine learning techniques employed in recent research for lung disease classification, focusing on pneumonia analysis using chest X-ray images. We explore conventional machine learning methods, pretrained deep learning models, customized convolutional neural networks (CNNs), and ensemble methods. A comprehensive comparison of different classification approaches is presented, encompassing data acquisition, preprocessing, feature extraction, and classification using machine vision, machine and deep learning, and explainable-AI (XAI). Our analysis highlights the superior performance of transfer learning-based methods using CNNs and ensemble models/features for lung disease classification. In addition, our comprehensive review offers insights for researchers in other medical domains too who utilize radiological images. By providing a thorough overview of various techniques, our work enables the establishment of effective strategies and identification of suitable methods for a wide range of challenges. Currently, beyond traditional evaluation metrics, researchers emphasize the importance of XAI techniques in machine and deep learning models and their applications in classification tasks. This incorporation helps in gaining a deeper understanding of their decision-making processes, leading to improved trust, transparency, and overall clinical decision-making. Our comprehensive review serves as a valuable resource for researchers and practitioners seeking not only to advance the field of lung disease detection using machine learning and XAI but also from other diverse domains.

IntroductionDeveloping efficient methods to infer relations among different faces consisting of numerous expressions or on the same face at different times (e.g., disease progression) is an open issue in imaging related research. In this study, we present a novel method for facial feature extraction, characterization, and identification based on classical computer vision coupled with deep learning and, more specifically, convolutional neural networks.MethodsWe describe the hybrid face characterization system named FRetrAIval (FRAI), which is a hybrid of the GoogleNet and the AlexNet Neural Network (NN) models. Images analyzed by the FRAI network are preprocessed by computer vision techniques such as the oriented gradient-based algorithm that can extract only the face region from any kind of picture. The Aligned Face dataset (AFD) was used to train and test the FRAI solution for extracting image features. The Labeled Faces in the Wild (LFW) holdout dataset has been used for external validation.Results and discussionOverall, in comparison to previous techniques, our methodology has shown much better results on k-Nearest Neighbors (KNN) by yielding the maximum precision, recall, F1, and F2 score values (92.00, 92.66, 92.33, and 92.52%, respectively) for AFD and (95.00% for each variable) for LFW dataset, which were used as training and testing datasets. The FRAI model may be potentially used in healthcare and criminology as well as many other applications where it is important to quickly identify face features such as fingerprint for a specific identification target.

Diabetes mellitus is a chronic metabolic complaint with numerous short- and long-term complications that harm a person’s physical and psychological health. Plumeria obtusa L. is a traditional medicine used in the treatment of diabetes to reduce complications related to behavior. Plumeria is a genus with antipsychotic activities. The objective of this study was to examine the effects of a methanolic extract of Plumeria obtusa L. in the attenuation of diabetes, on symptoms of Alzheimer disease, and on other associated behavioral aspects. A single dose of alloxan was administered to an experimental group of rats to induce development of diabetes (150 mg/kg, intraperitoneal) and the rats were then administered selected doses of methanolic extract of Plumeria obtusa L. (Po.Cr) or glibenclamide (0.6 mg/kg) for 45 consecutive days. Behavioral effects were evaluated using three validated assays of anxiety-related behavior: the open field test, the light and dark test, and the elevated plus maze. Anti-depressant effects of Plumeria obtusa L. were evaluated using the forced swim test (FST) and memory and learning were assessed using the Morris water maze (MWM) task. Po.Cr was also evaluated for phytochemicals using total phenolic content (TPC), total flavonoid content (TFC), and high-performance liquid chromatography assays, and antioxidant capability was assessed through assays of DPPH radical scavenging, total oxidation capacity, and total reducing capacity. In the alloxan-induced model of diabetes, the administration of Po.Cr and glibenclamide for 45 days produced a marked decrease (p < 0.001) in hyperglycemia compared to control animals. Po.Cr treatment also resulted in improvement in indicators, such as body weight and lipid profile (p < 0.05), as well as restoration of normal levels of alanine transaminase (ALT) (p < 0.001), a biomarker of liver function. Diabetic rats presented more Alzheimer-like symptoms, with greater impairment of memory and learning, and increased anxiety and depression compared to non-diabetic normal rats, whereas treated diabetic rats showed significant improvements in memory and behavioral outcomes. These results demonstrate that Po.Cr reversed alloxan-induced hyperglycemia and ameliorated Alzheimer-related behavioral changes, which supports additional study and assessment of conventional use of the plant to treat diabetes and associated behavioral complications.

A new methodology, the hybrid learning system (HLS), based upon semi-supervised learning is proposed. HLS categorizes hyperspectral images into segmented regions with discriminative features using reduced training size. The technique utilizes the modified breaking ties (MBT) algorithm for active learning and unsupervised learning-based regressors, viz. multinomial logistic regression, for hyperspectral image categorization. The probabilities estimated by multinomial logistic regression for each sample helps towards improved segregation. The high dimensionality leads to a curse of dimensionality, which ultimately deteriorates the performance of remote sensing data classification, and the problem aggravates further if labeled training samples are limited. Many studies have tried to address the problem and have employed different methodologies for remote sensing data classification, such as kernelized methods, because of insensitiveness towards the utilization of large dataset information and active learning (AL) approaches (breaking ties as a representative) to choose only prominent samples for training data. The HLS methodology proposed in the current study is a combination of supervised and unsupervised training with generalized composite kernels generating posterior class probabilities for classification. In order to retrieve the best segmentation labels, we employed Markov random fields, which make use of prior labels from the output of the multinomial logistic regression. The comparison of HLS was carried out with known methodologies, using benchmark hyperspectral imaging (HI) datasets, namely “Indian Pines” and “Pavia University”. Findings of this study show that the HLS yields the overall accuracy of {99.93% and 99.98%}Indian Pines and {99.14% and 99.42%}Pavia University for classification and segmentation, respectively.