Piyushsamantpth

Fine-tunning-of-SAM-for-specific-segmentation-application

GenAI-mirna-Data-

pathology-Features-and-differenciation-

Dicom-to-png-with-hu-windowing-

Nested-Fold-corss-validation

The function is particularly useful for evaluating model performance in scenarios where hyperparameter tuning is critical and overfitting is a concern.

Modified-_T-test-for-feature-selection

This code is written in matlab. The modified t-test can be used to choose the most relevant features from a larger set depending on their statistical attributes. This code is also suitable for multiclass problems.

Yolo-for-different-traffic-signal-detection-

different-classifierfeature-based-

test

Piyushsamantpth/test

Piyushsamantpth/pathology-Features-and-differenciation-

Piyushsamantpth/different-classifierfeature-based-

Piyushsamantpth/Yolo-for-different-traffic-signal-detection-

Piyushsamantpth/GenAI-mirna-Data-

Piyushsamantpth/Dicom-to-png-with-hu-windowing-

Piyushsamantpth/Fine-tunning-of-SAM-for-specific-segmentation-application

Piyushsamantpth/Nested-Fold-corss-validation

The function is particularly useful for evaluating model performance in scenarios where hyperparameter tuning is critical and overfitting is a concern.

Piyushsamantpth/Modified-_T-test-for-feature-selection

This code is written in matlab. The modified t-test can be used to choose the most relevant features from a larger set depending on their statistical attributes. This code is also suitable for multiclass problems.