A Comparative Review of Machine Learning and Deep Learning Models for Lung Disease Classification Across Medical Imaging Modalities

The increasing integration of artificial intelligence into medical imaging has significantly advanced lung disease detection using chest X-rays and related modalities. This survey conducts a comprehensive comparative analysis of 12 researchworkspublishedbetween 2021 and 2025, with emphasis on five key studies employing machine learning, deep learning, and hybrid algorithms for image-based lung disease classification. Design/methodology/approach–Thereviewexaminesmethodologiesinvolving segmentation-classification pipelines, attentionenhanced CNNs, hybrid CNN–ELM architectures, multi-branch deep networks, and fusion models combining handcrafted and deep features. Datasets used across these studies include NIH ChestX-ray14, JSRT, MC, LC25000, SIIM-ACR, LIDCIDRI,andCOVID-19imagerepositories.Eachalgorithmisanalyzedinterms of preprocessing strategies, model design, evaluation metrics, computational efficiency, and applicability to different imaging types such as chest X-rays, CT scans, and histopathologyslides. Findings – The analysis shows thatnosinglearchitectureperformsbestacrossalltasks;instead, each model demonstrates unique advantages depending on data modality and clinical objective. Attention-based CNNs excel in feature refinement, hybrid CNNELM models offer lightweight computation, and dual-branch frameworks improve multi-label chest disease detection.. Originality/value – By consolidating state-of-the-art image-based lung disease classification research, this survey highlights methodological strengths, limitations, and emerging trends. Although key research gaps are identified for future inclusion, the present review provides a structured foundation for developing more scalable and reliable diagnostic systems.