RENAL CYST DETECTION IN ABDOMINAL MRI IMAGES USING DEEP LEARNING SEGMENTATION

Renal cysts are categorized as simple cysts and complex cysts. Simple cysts are harmless and complicated cysts are cancerous and leading to a dangerous situation. The study aims to implement a deep learning-based segmentation that uses the Renal images to segment the cyst, detecting the size of the cyst and assessing the state of cyst from the infected renal image. The automated method for segmenting renal cysts from MRI abdominal images is based on a U-net algorithm. The deep learning-based segmentation like U-net algorithm segmented the renal cyst. The characteristics of the segmented cyst were analyzed using the Statistical features extracted using GLCM algorithm. The machine learning classification is performed using the extracted GLCM features. Three machine learning classifiers such as Naive Bayes, Hoeffding Tree and SVM are used in the proposed study. Naive Bayes and Hoeffding Tree achieved the highest accuracy of 98%. The SVM classifier achieved 96% of accuracy. This study proposed a new system to diagnose the renal cyst from MRI abdomen images. Our study focused on cyst segmentation, size detection, feature extraction and classification. The three-classification method suits best for classifying the renal cyst. Naive Bayes and Hoeffding Tree classifier achieved the highest accuracy. The diameter of cyst size is measured using the blobs analysis method to predict the renal cyst at an earlier stage. Hence, the deep learning-based segmentation performed well in segmenting the renal cyst and the three classifiers achieved the highest accuracy, above 95%.

Automated summary

This study aims to use deep learning-based segmentation on renal images to detect and assess the size and state of renal cysts. The U-net algorithm is used for cyst segmentation, and statistical features are extracted using the GLCM algorithm for analysis and machine learning classification. In the proposed study, Naive Bayes and Hoeffding Tree classifiers achieved the highest accuracy of 98%, while the SVM classifier achieved 96% accuracy.