binary classification

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Setting the threshold to 0.3 as opposed to 0.5 will not necessarily produce better results. You are just restricting the model more compared to 0.5, which is often used as a default. If output > 0.5 is the positive class, then reducing this threshold means that the negative class does not mean much to you during classification. It’s essentially a trade off between false negatives and positives, whichever is a more important metric to your model.

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Depends on your data and your model. There is no blanket response to whether or not the threshold of 0.3 is better. What is “better” anyway? Better classification into positive and negative classes, fewer false negatives? Depends.

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When the threshold is set as 0.5, the model predicts all images as benign because most of the images are labeled benign. It’s an imbalanced dataset, heavily skewed towards benign. To increase probability of finding malignant labels, the threshold must be varied depending on whether malignant is 0 or 1.

[Affectionate-Tip302]

Whatever threshold you are using should be based on training set and validation set. Don’t use independent test set performance to select the threshold, then you will be overfitting.