Optimizing the Powerhouse: Fine-Tuning CNNs for Superior Lung Disorder Detection

Present work involves the use of deep learning models, in particular, Convolutional Neural Networks, to detect critical diseases of the lungs such as pneumonia, tuberculosis, and lung cancer through the analysis of chest X-ray and CT scan images. CNN has emerged as a robust tool for the analysis of medical images due to their natural ability to automatically extract hierarchical features, hence making them ideal for complicated tasks such as disease detection. Well-known architectures in this work are VGG16 and VGG19; they are combined with specially designed sequential and functional models to solve this task of recognizing those diseases. These models are trained on open-source data that will provide the model with real-world medical images for it to learn from diverse cases of lung abnormalities. One important enhancement in pursuit of model performance and robustness in this work is the employment of augmentation techniques. Since, in most cases, labeled medical data comes in a limited amount, augmentation is an important way of artificially increasing the effective size of the training set to allow the models to generalize better on unseen data. The images were rescaled, shear transformed, and flipped horizontally to simulate different orientations and variations that can arise during real-world X-ray and CT scans. This will also make the models less sensitive to changes in orientation, scale, or noise of an image and enhance their capability of detecting diseases more precisely in different datasets. The results of the study show that early detection by deep learning models has improved a lot by incorporating advanced CNN architectures and augmentation strategies. Those customized sequential and functional models performed very well, especially in the classification of lung diseases with very high precision. This is indeed important for early diagnosis because early identification of diseases like pneumonia, tuberculosis, and lung cancer can lead to better treatment plans and improved patient outcomes. Moreover, the study highlights how deep learning models may represent the most viable tool in clinical practice to provide not only accurate but timely diagnostic information that would support medical professionals most proficiently.