In medical imaging, deep learning-based techniques have drawn a lot of attention, especially when it comes to exploiting red blood cell (RBC) images to diagnose malaria. There is much promise in these techniques for differentiating malaria unhealthy cells from healthy cells. However, the performance of a traditional deep studying models has been hampered with the aid of the problems of managing small, hazy inflamed regions in RBC pics in addition to the scarcity of malaria datasets. To address the ones boundaries, we advocate a novel neural network technique that identifies and highlights infected areas inner RBC images, thereby enhancing the version’s capacity to successfully classify in parasite versus healthy cells. This technique complements feature extraction by using focusing on the regions maximum responsible for ailment identification, leading to stepped forward type overall performance. Tested on the NIH Malaria dataset (sample dataset), the proposed technique appreciably increases type accuracy throughout models of various complexity, which includes ResNet and other deep architectures. The consequences demonstrate that our technique outperforms baseline strategies and offers a stronger answer for malaria detection in scientific imaging, especially in records-scarce and complex imaging environments.