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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.

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The local navigation problem for autonomous mobile robots (AMRs) and its applications to wheeled robots is addressed. The problem of driving an AMR to a goal in an unknown environment, containing both stationary as well as moving obstacles, is formulated as a dynamic feedback control problem. An algorithm using local feedback information to generate subgoals for driving the AMR along a collision -free trajectory to the goal is adopted. The local free -space for subgoal selections is constructed taking into account the locally visible obstacles and the AMR operating limits A dynamic model of wheeled robots based on driving and steering mechanisms is derived. A controller design based on a self-tuning pole assignment approach is presented for motion reference trajectory tracking. Integration of local sensor data, system dynamics and operating constraints with a developed deision support system, for steering and control, is performed to produce the appropriate intelligent navigation decisions. Finally, the effectiveness of the navigation and control strategies in directing the AMR along a collision -free trajectory to the final goal in a finite time, is illustrated, by simulation.

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