Malaria in the rural and remote regions of tropical countries remain a major public health challenge. Early diagnosis and prompt effective treatment are the basis for the management of malaria and for reducing malaria mortality and morbidity worldwide and the key to malaria elimination. While Rapid Diagnostic Test (RDT) remains the current mainstay testing malaria infections, it is usually used in conjunction with clinical findings and lab tests of blood films through Microscopy, the gold standard of malaria diagnosis. Recent reports suggest that the accuracy of RDTs could be compromised due to parasite antigen gene deletion(s), and the lack of expertise and high turnover time makes microscopy impractical to be used in rural and remote areas which impede the diagnosis and treatment of the disease. Delay in receiving treatment for uncomplicated malaria is reported to increase the risk of developing severe malaria and mortality. Thus, the need to develop advanced, faster, and smarter tools for malaria diagnosis is paramount, specially to reinforce the gold standard method, i.e., malaria microscopy which is a full-proof tool given the limitations be addressed. Deep learning-based methods have proven to provide human expert level performance on object detection/classification on image data. Such methods can be utilized for automation of repetitive task in assessing large number of microscope images of blood samples. In this paper, we propose a novel approach to improve the performance of deep learning models through consistent labelling of ground truth bounding box for the task of pathogen detection on microscope images of thick blood smears. Recommendations are made on the reliability and repeatability testing of the trained models. A custom deep learning architecture (YOLO-mp) is developed based on the design criteria of optimizing accuracy and speed of detection with minimal resources. The custom three-layered YOLO-mp-3l and four-layered YOLO-mp-4l models achieved the best mAP scores of 93.99 (@IoU=0.5) and 94.07 (@IoU=0.5), respectively outperforming standard YOLOv4 (mAP 92.56 @IoU=0.5) for detection of malaria pathogen on a public dataset of thick smear microscopic images captured using phone camera. YOLO-mp-3l (BFLOPs= 21.8, model size=24.5Mb) and YOLO-mp-4l (BFLOPs=24.477, model size=25.4Mb) outperformed standard YOLOv4 (BFLOPs=127.232, model size=244Mb) in terms of computation and memory requirements proving them suitable to run on low resource devices.