M.S. AAI Capstone Chronicles 2024

CNN Lung Disease Classification

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the amount of learning that can be applied to the exact localization of the diseases, potentially affecting the model’s ability to generalize and accurately detect conditions in new, unannotated X-rays. Below is a sample image from our dataset, illustrating a bounding box highlighting an area of Atelectasis:

Minimal data augmentation techniques were applied to preserve the underlying characteristics of the chest X-ray images while still enhancing the model's generalization capability. The augmentation includes subtle transformations such as random rotation, contrast adjustment, translation, and zoom, each with a very low intensity. This careful approach ensures that the essential diagnostic features of the X-ray images remain intact, while also providing the model with a slightly varied dataset to improve its robustness against small changes in the input images.

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