Erdem, KenanKobat, Mehmet AliBilen, Mehmet NailBalik, YunusAlkan, SevimCavlak, FeyzanurAcharya, U. Rajendra2026-03-262026-03-2620230899-94571098-109810.1002/ima.229142-s2.0-85161089102https://doi.org/10.1002/ima.22914https://hdl.handle.net/20.500.14901/3270Erdem, Kenan/0000-0001-6002-5873; Poyraz, Ahmet Kursad/0000-0001-8992-1743; Barua, Prabal Datta/0000-0001-5117-8333; Erten, Mehmet/0000-0002-6664-4568; Dogan, Sengul/0000-0001-9677-5684; Acharya, U Rajena/0000-0003-2689-8552; Bilen, Mehmet Nail/0000-0003-1468-2930COVID-19, chronic obstructive pulmonary disease (COPD), heart failure (HF), and pneumonia can lead to acute respiratory deterioration. Prompt and accurate diagnosis is crucial for effective clinical management. Chest X-ray (CXR) and chest computed tomography (CT) are commonly used for confirming the diagnosis, but they can be time-consuming and biased. To address this, we developed a computationally efficient deep feature engineering model called Hybrid-Patch-Alex for automated COVID-19, COPD, and HF diagnosis. We utilized one CXR dataset and two CT image datasets, including a newly collected dataset with four classes: COVID-19, COPD, HF, and normal. Our model employed a hybrid patch division method, transfer learning with pre-trained AlexNet, iterative neighborhood component analysis for feature selection, and three standard classifiers (k-nearest neighbor, support vector machine, and artificial neural network) for automated classification. The model achieved high accuracy rates of 99.82%, 92.90%, and 97.02% on the respective datasets, using kNN and SVM classifiers.eninfo:eu-repo/semantics/openAccessAlexnetBiomedical Image ClassificationCT Image ClassificationHybrid-Patch-AlexTransfer LearningArticle