Unveiling the Optimal CNN- A Performance Evaluation of Architectures in Malaria Image Classification

Malaria is still a substantial health constraint in the world, particularly within the endemic region, where precisely and timely diagnosis becomes a vital task to make significant control of the disease. Conventional microscopic analysis of blood film the gold standard is work intensive, tedious, and dependent on human technicians' expertise. These constraints often impede timely diagnosis, especially in resource-limited environments. The arise of deep learning, specially in Convolutional Neural Networks (CNNs), has enabled automation along with improved malaria diagnosis from microscopic blood cell imagery. As there are many CNN architectures, determining which neural network is most suitable for the application we are concerned with remains an unsolved problem. This work presents an exhaustive comparative study of several major CNNs ResNet,DenseNet, Inception, VGG, and MobileNetV to evaluate and analyze their performance to classify malaria-infected and uninfected blood cells. Leveraging a large dataset of microscopic blood cell images, we benchmark each architecture using our Multi Metric protocol, which includes accuracy, sensitivity, specificity, F1-score, and Area Under the Curve (AUC). Carrying out cost calculation. The primary objective of this study is to incorporate computational effort, model complexity, and learning time into its real-world applicability. The results are expected to reveal the best CNN model in terms of both superior diagnostic performance and practical convenience for general acceptance in clinical practice and ultimately in public health, leading to more efficient and accessible malaria detection approaches worldwide