As detection of cancer and diabetes diseases has captured a great attention in the field of biosensing, many ways were introduced to detect these diseases with high accuracy. This paper presents a novel use of zinc selenide (ZnSe) semiconductor material to enhance the sensitivity of a conventional Ag-based surface plasmon resonance biosensor. The proposed sensor structure is simple (BK7 prism/Ag/ZnSe), cost-effective, and easy to design and fabricate. The sensor is tested in the detection of different cancerous cells in human blood, showing an excellent sensitivity of 359 $$^\circ$$∘ /RIU. In addiion, it is tested to detect various glucose levels in human urine, and it provides a very high performance with a sensitivity of 366.6 $$^\circ$$∘ /RIU, which is 70% better than that of a similar reported work. Thus, the proposed biosensor is capable efficiently to detect various kinds of cancer cells and different glucose concentrations in urine.

Acute lower respiratory infection is a leading cause of death in developing countries. Hence, progress has been made for early detection and treatment. There is still a need for improved diagnostic and therapeutic strategies, particularly in resource-limited settings. Chest X-ray and computed tomography (CT) have the potential to serve as effective screening tools for lower respiratory infections, but the use of artificial intelligence (AI) in these areas is limited. To address this gap, we present a computer-aided diagnostic system for chest X-ray and CT images of several common pulmonary diseases, including COVID-19, viral pneumonia, bacterial pneumonia, tuberculosis, lung opacity, and various types of carcinoma. The proposed system depends on super-resolution (SR) techniques to enhance image details. Deep learning (DL) techniques are used for both SR reconstruction and classification, with the InceptionResNetv2 model used as a feature extractor in conjunction with a multi-class support vector machine (MCSVM) classifier. In this paper, we compare the proposed model performance to those of other classification models, such as Resnet101 and Inceptionv3, and evaluate the effectiveness of using both softmax and MCSVM classifiers. The proposed system was tested on three publicly available datasets of CT and X-ray images and it achieved a classification accuracy of 98.028% using a combination of SR and InceptionResNetv2. Overall, our system has the potential to serve as a valuable screening tool for lower respiratory disorders and assist clinicians in interpreting chest X-ray and CT images. In resource-limited settings, it can also provide a valuable diagnostic support.