Accurate malaria detection in resource-limited settings requires robust solutions—here, we introduce a selective intensity ensemble classifier (SIEC) that applies a triple-threshold strategy for enhanced microscopic image classification. This involves training three separate convolutional neural network models on the same images processed with different pixel-intensity thresholds: original, pixels above 100, and pixels above 200. This approach enables the ensemble to capture complementary low-, mid-, and high-intensity features, enhancing feature diversity and classification accuracy. The experiments were conducted on the publicly available Malaria Cell Dataset, consisting of 27,558 images. The proposed SIEC achieved an accuracy of 95.09%, with a precision of 95.27%, and matching recall and F1 scores of 95.09%, consistently outperforming six standard CNN models, including ResNet50, VGG16, Inception, and MobileNetV2. Notably, the combination of the 100-pixel filtered and original images yielded the highest classification performance, demonstrating the ensemble’s ability to integrate detailed and abstracted features effectively. These findings highlight SIEC as a promising and scalable solution for automated malaria detection and broader diagnostic imaging tasks.