Kidney cancer is one of the deadliest types of cancer affecting the human body. It’s regarded as the seventh most common type of cancer affecting men and the ninth affecting women. Early diagnosis of kidney cancer can improve the survival rates for many patients. Clear cell renal cell carcinoma (ccRCC) accounts for 90% of renal cancers. Although the exact cause of the kidney cancer is still unknown, early diagnosis can help patients get the proper treatment at the proper time. In this paper, a novel semi-automated model is proposed for early detection and staging of clear cell renal cell carcinoma. The proposed model consists of three phases: segmentation, feature extraction, and classification. The first phase is image segmentation phase where images were masked to segment the kidney lobes. Then the masked images were fed into watershed algorithm to extract tumor from the kidney. The second phase is feature extraction phase where gray level co-occurrence matrix (GLCM) method was integrated with normal statistical method to extract the feature vectors from the segmented images. The last phase is the classification phase where the resulted feature vectors were introduced to random forest (RF) and support vector machine (SVM) classifiers. Experiments have been carried out to validate the effectiveness of the proposed model using TCGA-KRIC dataset which contains 228 CT scans of ccRCC patients where 150 scans were used for learning and 78 for validation. The proposed model showed an outstanding improvement of 15.12% for accuracy from the previous work.