AbstractCOVID‐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.