3D CT Liver Images Lesion Extraction and Classification for Using 3D-CNN with GLRLM

The imaging technique known as computed tomography (CT) is often considered to be the most reliable way for non-invasive diagnosis. Through the use of three-dimensional (3D) computed tomography images, we were able to categorize aggressive tumors that were found in the liver for the aim of this research. When it comes to the three-dimensional (3D) CT image, the dimension reduction is accomplished by the use of principal component analysis (PCA). The Random Forest Algorithm is responsible for identifying the abnormal hepatic region, and a median filter is used to reduce the amount of noise occurring. Methods of data augmentation are also employed in this process. To extract Gray Level Run Length Matrix (GLRLM) features, using Pyradiomics python package. These features are then fed into a 3D-CNN classifier to classify malignant tumors and haemangiomas. Our model was found to have greater performance after being compared to the findings of other models. This was revealed after the comparison was made. For the training dataset, our model has an accuracy of 97.2%, and for the test dataset, it has an accuracy of 98.25%. This accuracy is determined by the performance metrics that our model incorporates.