Malaria Detection Using an Improved AlexNet-Based Deep Learning Model

• Author(s): M. Phanindra; K. Venkata Siva Sai Sreeja; P. Nikhil
• Paper ID: 1717227
• Page: 472-474
• Published Date: 07-05-2026
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 9 Issue 11 May-2026

Abstract

Diagnosing malaria still relies heavily on microscope examination of blood smears — which works, but is slow, hard to scale, and only as good as the person doing it. This paper describes a deep learning pipeline for binary malaria cell screening built on a modified AlexNet CNN. We used the public NIH/Kaggle malaria cell-image dataset, resized everything to 100×100 RGB patches, applied live augmentation, and trained a regularized CNN with batch normalization and dropout. Beyond just reporting accuracy, we evaluated the model with confusion matrices, ROC-AUC, precision-recall curves, calibration plots, and Grad-CAM heatmaps. We also compare the approach against a recent two-stage YOLOv4/DenseNet-121 system. This model doesn't do whole-slide detection or species identification — it's a clean, reproducible infected-vs-uninfected screener that can be understood, explained, and built on.

Keywords

Malaria Detection, Alexnet, Convolutional Neural Network, Grad-CAM, Medical Image Classification, Binary Screening.

Citations

IRE Journals:
M. Phanindra, K. Venkata Siva Sai Sreeja, P. Nikhil "Malaria Detection Using an Improved AlexNet-Based Deep Learning Model" Iconic Research And Engineering Journals Volume 9 Issue 11 2026 Page 472-474 https://doi.org/10.64388/IREV9I11-1717227

IEEE:
M. Phanindra, K. Venkata Siva Sai Sreeja, P. Nikhil "Malaria Detection Using an Improved AlexNet-Based Deep Learning Model" Iconic Research And Engineering Journals, 9(11) https://doi.org/10.64388/IREV9I11-1717227