AAI_2025_Capstone_Chronicles_Combined

Fig 4. Co-Occurrence Matrix

Finally, our correlation plots and disease frequency analyses revealed strong associations between certain pathologies; for example, pleural effusion frequently co-occurred with atelectasis. These patterns confirmed the need to treat the task as a multi-label classification problem, since many chest X-rays were presented with more than one condition. As a result, both of our CNN architectures were designed with a single output layer using sigmoid activation, enabling the model to predict multiple conditions in parallel. This approach was very important because it reflected the clinical reality that differing medical diseases often appear together and allow the system to flag co-occurring pathologies within a single inference, which would not be possible if the task were broken into separate binary classifiers.

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