Less Is More: An Explainable AI Framework for Lightweight Malaria Classification

TL;DR

This paper presents a lightweight, interpretable machine learning pipeline for malaria cell classification that achieves high accuracy comparable to deep learning models but with significantly lower computational requirements, making it suitable for resource-limited settings.

Contribution

The study introduces the EMFE pipeline, combining simple morphological features with lightweight models to match deep learning performance in malaria classification.

Findings

Logistic Regression achieved 94.80% accuracy with minimal file size and inference time.

Ensemble model improved accuracy to 97.15%.

Deep learning models require much larger storage and longer inference times.

Abstract

Background and Objective: Deep learning models have high computational needs and lack interpretability but are often the first choice for medical image classification tasks. This study addresses whether complex neural networks are essential for the simple binary classification task of malaria. We introduce the Extracted Morphological Feature Engineered (EMFE) pipeline, a transparent, reproducible, and low compute machine learning approach tailored explicitly for simple cell morphology, designed to achieve deep learning performance levels on a simple CPU only setup with the practical aim of real world deployment. Methods: The study used the NIH Malaria Cell Images dataset, with two features extracted from each cell image: the number of non background pixels and the number of holes within the cell. Logistic Regression and Random Forest were compared against ResNet18, DenseNet121, MobileNetV2, and EfficientNet across accuracy, model size, and CPU inference time. An ensemble model was created by combining Logistic Regression and Random Forests to achieve higher accuracy while retaining efficiency. Results: The single variable Logistic Regression model achieved a test accuracy of 94.80 percent with a file size of 1.2 kB and negligible inference latency (2.3 ms). The two stage ensemble improved accuracy to 97.15 percent. In contrast, the deep learning methods require 13.6 MB to 44.7 MB of storage and show significantly higher inference times (68 ms). Conclusion: This study shows that a compact feature engineering approach can produce clinically meaningful classification performance while offering gains in transparency, reproducibility, speed, and deployment feasibility. The proposed pipeline demonstrates that simple interpretable features paired with lightweight models can serve as a practical diagnostic solution for environments with limited computational resources.