Computer-aided diagnosis of cancer based on endoscopic image analysis is a promising area in the field of computer vision and machine learning. Convolutional neural networks are one of the most popular approaches in the endoscopic image analysis. The paper presents an endoscopic video analysis algorithm based on the use of convolutional neural network. To analyze the quality of the algorithm on the video data from the endoscope, the intersection over union (IoU) metric for object detection is used. The experimental results shows that the average value of IoU coefficient for the developed algorithm is 0.767, which corresponds to a high degree of intersection of areas identified by an expert and the algorithm.
The paper considers bimodal person identification problem by analyzing the speaker’s face and voice. Two speaker identification algorithms are developed and compared. The idea of the first algorithm consists of extracting features from the speech signal in the form of mel frequency cepstral coefficients and, with this basis, forming a speaker model using Gaussian mixtures. Second approach is based on the use of a universal background model obtained from the records of a large number of speakers. For face identification, a neural network with 13 convolutional layers was used. For the learning and testing, the databases of speech signals and face images of 100 people were formed. The final bimodal identification system shows the high level of accuracy identification of more than 95%. The results of this experiment demonstrated the possibility of applying the proposed algorithms to the person identification problem in real-life systems.