Wireless Capsule Endoscopy (WCE) is a standard non-invasive modality for small bowel examination. Recently, the development of computer-aided diagnosis (CAD) systems for gastrointestinal (GI) bleeding detection in WCE image videos has become an active research area with the goal of relieving the workload of physicians. Existing methods based primarily on handcrafted features usually give insufficient accuracy for bleeding detection, due to their limited capability of feature representation. In this paper, we present a new automatic bleeding detection strategy based on a deep convolutional neural network and evaluate our method on an expanded dataset of 10,000 WCE images. Experimental results with an increase of around 2 percentage points in the Fi score demonstrate that our method outperforms the state-of-the-art approaches in WCE bleeding detection. The achieved Fi score is of up to 0.9955.
Tracking control is a fundamentally important issue for robot and motor systems, where smooth velocity commands are desirable for safe and effective operation. In this paper, a novel biologically inspired tracking control approach to real-time navigation of a nonholonomic mobile robot is proposed by integrating a backstepping technique and a neurodynamics model. The tracking control algorithm is derived from the error dynamics analysis of the mobile robot and the stability analysis of the closed-loop control system. The stability of the robot control system and the convergence of tracking errors to zeros are guaranteed by a Lyapunov stability theory. Unlike some existing tracking control methods for mobile robots whose control velocities suffer from velocity jumps, the proposed neurodynamics-based approach is capable of generating smooth continuous robot control signals with zero initial velocities. In addition, it can deal with situations with a very large tracking error. The effectiveness and efficiency of the proposed neurodynamics-based tracking control of mobile robots are demonstrated by experimental and comparison studies.
A neural dynamics based approach is proposed for real-time motion planning with obstacle avoidance of a mobile robot in a nonstationary environment. The dynamics of each neuron in the topologically organized neural network is characterized by a shunting equation or an additive equation. The real-time collision-free robot motion is planned through the dynamic neural activity landscape of the neural network without any learning procedures and without any local collision-checking procedures at each step of the robot movement. Therefore the model algorithm is computationally simple. There are only local connections among neurons. The computational complexity linearly depends on the neural network size. The stability of the proposed neural network system is proved by qualitative analysis and a Lyapunov stability theory. The effectiveness and efficiency of the proposed approach are demonstrated through simulation studies.
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