The COVID-19 pandemic has a significant negative effect on peoples health, as well as on the worlds economy. Polymerase chain reaction (PCR) is one of the main tests used to detect COVID-19 infection. However, it is expensive, time-consuming, and lacks sufficient accuracy. In recent years, convolutional neural networks have grabbed many researchers attention in the machine learning field, due to its high diagnosis accuracy, especially the medical image recognition. Many architectures such as Inception, ResNet, DenseNet, and VGG16 have been proposed and gained an excellent performance at a low computational cost. Moreover, in a way to accelerate the training of these traditional architectures, residual connections are combined with inception architecture. Therefore, many hybrid architectures such as Inception-ResNetV2 are further introduced. This paper proposes an enhanced Inception-ResNetV2 deep learning model that can diagnose chest X-ray (CXR) scans with high accuracy. Besides, a Grad-CAM algorithm is used to enhance the visualization of the infected regions of the lungs in CXR images. Compared with state-of-the-art methods, our proposed paper proves superiority in terms of accuracy, recall, precision, and F1-measure.

Background: Alzheimer’s disease (AD) is a progressive neurological disorder that significantly affects middle-aged and elderly adults, leading to cognitive deterioration and hindering daily activities. Notwithstanding progress, conventional diagnostic techniques continue to be susceptible to inaccuracies and inefficiencies. Timely and precise diagnosis is essential for early intervention. Methods: We present an enhanced hybrid deep learning framework that amalgamates the EfficientNetV2B3 with Inception-ResNetV2 models. The models were integrated using an adaptive weight selection process informed by the Cuckoo Search optimization algorithm. The procedure commences with the pre-processing of neuroimaging data to guarantee quality and uniformity. Features are subsequently retrieved from the neuroimaging data by utilizing the EfficientNetV2B3 and Inception-ResNetV2 models. The Cuckoo Search algorithm allocates weights to various models dynamically, contingent upon their efficacy in particular diagnostic tasks. The framework achieves balanced usage of the distinct characteristics of both models through the iterative optimization of the weight configuration. This method improves classification accuracy, especially for early-stage Alzheimer’s disease. A thorough assessment was conducted on extensive neuroimaging datasets to verify the framework’s efficacy. Results: The framework attained a Scott’s Pi agreement score of 0.9907, indicating exceptional diagnostic accuracy and dependability, especially in identifying the early stages of Alzheimer’s disease. The results show its superiority over current state-of-the-art techniques.Conclusions: The results indicate the substantial potential of the proposed framework as a reliable and scalable instrument for the identification of Alzheimer’s disease. This method effectively mitigates the shortcomings of conventional diagnostic techniques and current deep learning algorithms by utilizing the complementing capabilities of EfficientNetV2B3 and Inception-ResNetV2 by using an optimized weight selection mechanism. The adaptive characteristics of the Cuckoo Search optimization facilitate its application across many diagnostic circumstances, hence extending its utility to a wider array of neuroimaging datasets. The capacity to accurately identify early-stage Alzheimer’s disease is essential for facilitating prompt therapies, which are crucial for decelerating disease development and enhancing patient outcomes.