The traditional way of diagnosing malaria takes time, as physicians have to check about 5000 cells to produce the final report. The accuracy of the final report also depends on the physician’s expertise. In the event of a malaria epidemic, a shortage of qualified physicians can become a problem. In the manual method, the parasites are identified by visual identification; this technique can be automated with the use of new algorithms. There are numerous publicly available image datasets containing the intricate structure of parasites, and deep learning algorithms can recognize these complicated patterns in the images. This study aims to identify and localize malaria parasites in the photograph of blood cells using the YOLOv5 model. In this research, a publicly available malaria trophozoite dataset is utilized which contains 1182 data samples. YOLOv5, with the novel technique of weight ensemble and traditional transfer learning, is trained using this dataset, and the results were compared with the other object detection models—for instance, Faster RCNN, SSD net, and the hybrid model. It was observed that YOLOv5 with the ensemble weights yields better results in terms of precision, recall, and mAP values: 0.76, 0.78, and 0.79, respectively. The mAP score closer to 1 signifies a higher confidence in localizing the parasites. This study is the first implementation of ensemble YOLOv5 in the malaria parasite detection field. The proposed ensemble model can detect the presence of malaria parasites and localize them with bounding boxes better than previously used models.

COVID-19 is an infectious disease caused by the deadly virus SARS-CoV-2 that affects the lung of the patient. Different symptoms, including fever, muscle pain and respiratory syndrome, can be identified in COVID-19-affected patients. The disease needs to be diagnosed in a timely manner, otherwise the lung infection can turn into a severe form and the patient’s life may be in danger. In this work, an ensemble deep learning-based technique is proposed for COVID-19 detection that can classify the disease with high accuracy, efficiency, and reliability. A weighted average ensemble (WAE) prediction was performed by combining three CNN models, namely Xception, VGG19 and ResNet50V2, where 97.25% and 94.10% accuracy was achieved for binary and multiclass classification, respectively. To accurately detect the disease, different test methods have been proposed and developed, some of which are even being used in real-time situations. RT-PCR is one of the most successful COVID-19 detection methods, and is being used worldwide with high accuracy and sensitivity. However, complexity and time-consuming manual processes are limitations of this method. To make the detection process automated, researchers across the world have started to use deep learning to detect COVID-19 applied on medical imaging. Although most of the existing systems offer high accuracy, different limitations, including high variance, overfitting and generalization errors, can be found that can degrade the system performance. Some of the reasons behind those limitations are a lack of reliable data resources, missing preprocessing techniques, a lack of proper model selection, etc., which eventually create reliability issues. Reliability is an important factor for any healthcare system. Here, transfer learning with better preprocessing techniques applied on two benchmark datasets makes the work more reliable. The weighted average ensemble technique with hyperparameter tuning ensures better accuracy than using a randomly selected single CNN model.