Cancer is characterized by abnormal cell growth and proliferation, which are both diagnostic indicators of the disease. When cancerous cells enter one organ, there is a risk that they may spread to adjacent tissues and eventually to other organs. Cancer of the cervix of the uterus often initially manifests itself in the uterine cervix, which is located at the very bottom of the uterus. Both the growth and death of cervical cells are characteristic features of this condition. False-negative results provide a significant moral dilemma since they may cause women to get an incorrect diagnosis of cancer, which in turn can result in the woman’s premature death from the disease. False-positive results do not raise any significant ethical concerns; but they do require a patient to go through an expensive and time-consuming treatment process, and they also cause the patient to experience tension and anxiety that is not warranted. In order to detect cervical cancer in its earliest stages in women, a screening procedure known as a Pap test is often performed. This article describes a technique for improving images using Brightness Preserving Dynamic Fuzzy Histogram Equalization. To individual components and find the right area of interest, the fuzzy c-means approach is applied. The images are segmented using the fuzzy c-means method to find the right area of interest. The feature selection algorithm is the ACO algorithm. Following that, categorization is carried out utilizing the CNN, MLP, and ANN algorithms.
In a video surveillance system, tracking multiple moving objects using a single camera feed is having numerous challenges. A multi-camera system increases the output image quality in both overlapping and non-overlapping environment. Traffic behavior analysis is an intensified demand in a recent topic of research. Due to increasing traffic in intercity roads, interstate, and national highways. Automated traffic visual surveillance applications with the multi-camera are a topic of research in computer vision. This paper, present a multi-camera system study for the overlapping area of the road for traffic analysis in three sections. The second section represents the thorough literature survey on the multi-camera system. Here, the third section is our proposed system using a dual-camera experimental setup with their coordination. A deep neural network is used in the experiments for traffic behavior analysis. The emphasis of this paper is on the physical arrangement of the multi-camera system, calibration, and advantages- disadvantages. On a conclusion note, future development and advancement in traffic analysis using a multi-camera system is discussed.
Visual surveillance emerged as an active automated research area of Computer Vision from the traditional mathematical approach to neural networks. A novel modified neural network technique for object detection and classification for input images and video feed from many cameras overlapping target areas is presented in this research.Modified Neural Network methodology represents layered architecture as the input, preprocessing and Operation layer, to simplify the processing needed to prepare for training neural networks. This strategy aids in delegating the tasks to layers with predefined tasks thus simplifying training, reducing computational requirements, and delivering performance. Two modules of the Neural Network will process the input. The first module is a modified Neural Network and will differ from traditional Neural Network in respect of connectivity between Neurons and their operations. This will still be Neural Network for data shared and threshold followed for marking differences – Markers, between the two inputs and simplified training. The second Module will be a traditional Neural Network for detection and classification that will track the detected objects. This paper proposed a system that provides the combined image as an output from multiple cameras feed using an untraditional Mathematical and Algorithmic Approach.
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