Pneumonia is a microorganism infection that causes chronic inflammation of the human lung cells. Chest X-ray imaging is the most well-known screening approach used for detecting pneumonia in the early stages. While chest-Xray images are mostly blurry with low illumination, a strong feature extraction approach is required for promising identification performance. A new hybrid explainable deep learning framework is proposed for accurate pneumonia disease identification using chest X-ray images. The proposed hybrid workflow is developed by fusing the capabilities of both ensemble convolutional networks and the Transformer Encoder mechanism. The ensemble learning backbone is used to extract strong features from the raw input X-ray images in two different scenarios: ensemble A (i.e., DenseNet201, VGG16, and GoogleNet) and ensemble B (i.e., DenseNet201, InceptionResNetV2, and Xception). Whereas, the Transformer Encoder is built based on the self-attention mechanism with multilayer perceptron (MLP) for accurate disease identification. The visual explainable saliency maps are derived to emphasize the crucial predicted regions on the input X-ray images. The end-to-end training process of the proposed deep learning models over all scenarios is performed for binary and multi-class classification scenarios. The proposed hybrid deep learning model recorded 99.21% classification performance in terms of overall accuracy and F1-score for the binary classification task, while it achieved 98.19% accuracy and 97.29% F1-score for multi-classification task. For the ensemble binary identification scenario, ensemble A recorded 97.22% accuracy and 97.14% F1-score, while ensemble B achieved 96.44% for both accuracy and F1-score. For the ensemble multiclass identification scenario, ensemble A recorded 97.2% accuracy and 95.8% F1-score, while ensemble B recorded 96.4% accuracy and 94.9% F1-score. The proposed hybrid deep learning framework could provide promising and encouraging explainable identification performance comparing with the individual, ensemble models, or even the latest AI models in the literature. The code is available here: https://github.com/chiagoziemchima/Pneumonia_Identificaton.

The COVID-19 pandemic has had a significant impact on many lives and the economies of many countries since late December 2019. Early detection with high accuracy is essential to help break the chain of transmission. Several radiological methodologies, such as CT scan and chest X-ray, have been employed in diagnosing and monitoring COVID-19 disease. Still, these methodologies are time-consuming and require trial and error. Machine learning techniques are currently being applied by several studies to deal with COVID-19. This study exploits the latent embeddings of variational autoencoders combined with ensemble techniques to propose three effective EVAE-Net models to detect COVID-19 disease. Two encoders are trained on chest X-ray images to generate two feature maps. The feature maps are concatenated and passed to either a combined or individual reparameterization phase to generate latent embeddings by sampling from a distribution. The latent embeddings are concatenated and passed to a classification head for classification. The COVID-19 Radiography Dataset from Kaggle is the source of chest X-ray images. The performances of the three models are evaluated. The proposed model shows satisfactory performance, with the best model achieving $99.19\%$ and $98.66\%$ accuracy on four classes and three classes, respectively.

In hyperspectral image (HSI) classification, Convolutional Neural Networks (CNNs) have exhibited exceptional performance, owing to their hierarchical nonlinear modeling. However, their fixed square receptive field constrains their ability to effectively handle irregular image regions. Graph Convolution Networks (GCNs) have been introduced to learn irregular regions through correlations between adjacent pixels modeled as superpixel-based nodes, yet they lack pixel-level information. We propose a novel approach "Pixel-level with Covariance Pooling feature and Superpixel-level feature Fusion for Hyperspectral Image Classification" (PiCovS). Our method harnesses complementary spectral-spatial features at both pixel and superpixel levels to capture characteristics of both small-scale regular and large-scale irregular regions. We introduce a hybrid network that integrates and propagates features between image-level pixels and graph-level nodes using a graph encoder-decoder, effectively reconciling the differences between regular CNN and irregular GCN data representations. To enhance superpixel boundary learning, we modify the Manifold Simple Linear Iterative Clustering (M-SLIC) algorithm by incorporating texture feature information, resulting in refined superpixel representations. Additionally, we propose a novel covariance pooling mechanism with an attention mechanism within the CNN branch, enabling the capturing and utilization of holistic HSI information along spectral and spatial dimensions by exploiting second-order statistics throughout the network. Our comprehensive experiments showcase the efficiency and robustness of the proposed framework, achieving an impressive overall accuracy of 99.84%, 99.97%, 99.98%, and 81.96% on the Indian Pines, University of Pavia, Salinas, and the Houston University datasets, respectively. Remarkably, PiCovS excels even with limited training samples, outperforming other state-of-the-art methods in accuracy.

Melanoma, recognized as the most lethal form of cancer, originates from the uncontrolled proliferation of melanocytes, cells responsible for melanin production. Identifying melanoma lesions poses a formidable challenge due to their visual resemblance to non-cancerous lesions. This paper presents an end-to-end skin lesion prediction framework that includes data collection, preprocessing, data augmentation, the state-of-the-art YOLOv8 prediction model, and evaluation procedure. To train and assess the proposed framework, two benchmark medical datasets were collected and used: (1) PH2 and (2) ISIC2018. The preprocessing procedure is conducted to improve prediction performance, and the augmentation process is used to increase the training set size to meet the requirements of the AI model. Subsequently, the state-of-the-art deep learning YOLOv8 model is adopted and fine-tuned as the core prediction component for the proposed framework. The detection results achieved an overall mAPs@0.5 of 99.50% and 97.30% using the PH2 and ISIC2018 datasets, respectively. These promising and encouraging evaluation results appear to be practically useful for establishing an end-to-end prediction framework for skin lesions.