Design and Analysis of a Deep Learning Ensemble Framework Model for the Detection of COVID-19 and Pneumonia Using Large-Scale CT Scan and X-ray Image Datasets

Explainable deep learning diagnostic system for prediction of lung disease from medical images

Around the globe, respiratory lung diseases pose a severe threat to human survival. Based on a central goal to reduce contiguous transmission from infected to healthy persons, several technologies have evolved for diagnosing lung pathologies. One of the emerging technologies is the utility of Artificial Intelligence (AI) based on computer vision for processing wide varieties of medical imaging but AI methods without explainability are often treated as a black box. Based on a view to demystifying the rationale influencing AI decisions, this paper designed and developed a novel low-cost explainable deep-learning diagnostic tool for predicting lung disease from medical images. For this, we investigated explainable deep learning (DL) models (conventional DL and vision transformers (ViTs)) for performing prediction of the existence of pneumonia, COVID19, or no-disease from both original and data augmentation (DA)-based medical images (from two chest X-ray datasets). The results show that our experimental consideration of the DA that combines the impact of cropping, rotation, and horizontal flipping (CROP+ROT+HF) for transforming input images and then passed as input to an Inception-V3 architecture yielded a performance that surpasses all the ViTs and other conventional DL approaches in most of the evaluated performance metrics. Overall, the results suggest that the utility of data augmentation schemes aided the DL methods to yield higher classification accuracies. Furthermore, we compared five different class activation mapping (CAM) algorithms (GradCAM, GradCAM++, EigenGradCAM, AblationCAM, and RandomCAM). The result shows that most of the examined CAM algorithms were effective in identifying the attention region containing the existence of pneumonia or COVID-19 from the medical images (chest X-rays). Our developed low-cost AI diagnostic tool (pilot system) can assist medical experts and radiographers in proffering early diagnosis of lung disease. For this, we selected five to seven deep learning models and the explainable algorithms were deployed on a novel web interface implemented via a Gradio framework.

Empowering COVID-19 detection: Optimizing performance through fine-tuned EfficientNet deep learning architecture

The worldwide COVID-19 pandemic has profoundly influenced the health and everyday experiences of individuals across the planet. It is a highly contagious respiratory disease requiring early and accurate detection to curb its rapid transmission. Initial testing methods primarily revolved around identifying the genetic composition of the coronavirus, exhibiting a relatively low detection rate and requiring a time-intensive procedure. To address this challenge, experts have suggested using radiological imagery, particularly chest X-rays, as a valuable approach within the diagnostic protocol. This study investigates the potential of leveraging radiographic imaging (X-rays) with deep learning algorithms to swiftly and precisely identify COVID-19 patients. The proposed approach elevates the detection accuracy by fine-tuning with appropriate layers on various established transfer learning models. The experimentation was conducted on a COVID-19 X-ray dataset containing 2000 images. The accuracy rates achieved were impressive of 99.55%, 97.32%, 99.11%, 99.55%, 99.11% and 100% for Xception, InceptionResNetV2, ResNet50 , ResNet50V2, EfficientNetB0 and EfficientNetB4 respectively. The fine-tuned EfficientNetB4 achieved an excellent accuracy score, showcasing its potential as a robust COVID-19 detection model. Furthermore, EfficientNetB4 excelled in identifying Lung disease using Chest X-ray dataset containing 4,350 Images, achieving remarkable performance with an accuracy of 99.17%, precision of 99.13%, recall of 99.16%, and f1-score of 99.14%. These results highlight the promise of fine-tuned transfer learning for efficient lung detection through medical imaging, especially with X-ray images. This research offers radiologists an effective means of aiding rapid and precise COVID-19 diagnosis and contributes valuable assistance for healthcare professionals in accurately identifying affected patients.

Health Recommendation System using Deep Learning-based Collaborative Filtering

The crucial aspect of the medical sector is healthcare in today's modern society. To analyze a massive quantity of medical information, a medical system is necessary to gain additional perspectives and facilitate prediction and diagnosis. This device should be intelligent enough to analyze a patient's state of health through social activities, individual health information, and behavior analysis. The Health Recommendation System (HRS) has become an essential mechanism for medical care. In this sense, efficient healthcare networks are critical for medical decision-making processes. The fundamental purpose is to maintain that sensitive information can be shared only at the right moment while guaranteeing the effectiveness of data, authenticity, security, and legal concerns. As some people use social media to recognize their medical problems, healthcare recommendation systems need to generate findings like diagnosis recommendations, medical insurance, medical passageway-based care strategies, and homeopathic remedies associated with a patient's health status. New studies aimed at the use of vast numbers of health information by integrating multidisciplinary data from various sources are addressed, which also decreases the burden and health care costs. This article presents a recommended intelligent HRS using the deep learning system of the Restricted Boltzmann Machine (RBM)-Coevolutionary Neural Network (CNN) that provides insights on how data mining techniques could be used to introduce an efficient and effective health recommendation systems engine and highlights the pharmaceutical industry's ability to translate from either a conventional scenario towards a more personalized. We developed our proposed system using TensorFlow and Python. We evaluate the suggested method's performance using distinct error quantities compared to alternative methods using the health care dataset. Furthermore, the suggested approach's accuracy, precision, recall, and F-measure were compared with the current methods.

A Novel partial sequence technique based Chaotic biogeography optimization for PAPR reduction in eneralized frequency division multiplexing waveform

For Orthogonal Frequency Division Multiplexing (OFDM) systems, the most significant problem is the peak-to-average power ratio. The utilisation of partial transmission sequence, often known as PTS, is an efficient method for reducing PAPR. When it comes to minimizing the peak-to-average power ratio (PAPR) in Orthogonal Frequency Division Multiplexing (OFDM) Systems, PTS is one of the most effective approaches that may be used. Due to the substantial data load, using peak-to-average power ratio in OFDM is challenging. The most crucial problem with OFDM is achieving better results by lowering PAPR. Provide a PTS in this research that is based on the Chaotic Biogeography Based Optimization (CBBO) algorithm to effectively address the high PAPR issue that exists in Generalized Frequency Division Multiplexing (GFDM) waveforms using Hermitian Symmetry property is used. The Hermitian symmetry is utilised in order to acquire a real-valued time-domain signal. Phase rotation factor combinations are carried out in an effective and optimal manner through the utilisation of an innovative combination of optimization techniques. In comparison to conventional optimization techniques, a new hybrid optimization offers quick convergence quality and minimal complexity. When compared to traditional PTS methods such traditional GFDM and OFDM-PTS, experimental results demonstrate that the suggested CBBO-PTS technique significantly improves on minimizing PAPR.