Hybrid GLFIL Enhancement and Encoder Animal Migration Classification for Breast Cancer Detection

Abstract

Breast cancer has become the second leading cause of death among women worldwide. In India, a woman is diagnosed with breast cancer every four minutes. There has been no known basis behind it, and detection is extremely challenging among medical scientists and researchers due to unknown reasons. In India, the ratio of women being identified with breast cancer in urban areas is 22:1. Symptoms for this disease are micro calcification, lumps, and masses in mammogram images. These sources are mostly used for early detection. Digital mammography is used for breast cancer detection. In this study, we introduce a new hybrid wavelet filter for accurate image enhancement. The main objective of enhancement is to produce quality images for detecting cancer sections in images. Image enhancement is the main step where the quality of the input image is improved to detect cancer masses. In this study, we use a combination of two filters, namely, Gabor and Legendre. The edges are detected using the Canny detector to smoothen the images. High-quality enhanced image is obtained through the Gabor-Legendre filter (GLFIL) process. Further image is used by classification algorithm. Animal migration optimization with neural network is implemented for classifying the image. The output is compared to existing filter techniques. Ultimately, the accuracy achieved by the proposed technique is 98%, which is higher than existing algorithms.

Artificial Neural Network Tuning by Improved Sine Cosine Algorithm for HealthCare 4.0

“…Therefore, the scientists have experimented with wide range of optimization algorithms on a variety of practical problems. Some of the most promising domains include medical diagnostics support [14,20,24,33,43], wireless sensor network tuning [4,9,12,48,57,66], stock price estimations [16], as well as intrusion detection and security domain [1,31,41,45,55,56,60,65] and plant classifying task [17].…”

Training a Logistic Regression Machine Learning Model for Spam Email Detection Using the Teaching-Learning-Based-Optimization Algorithm

“…A significant number of metaheuristics methods exist today, explained by the no free lunch theorem [57], stating that an universal solution to all optimization problems is not existing. Consequently, the researchers have implemented a large number of algorithms, and employed them on a wide range of real-world problems from different domains, such as medical diagnostics [15,21,25,37,47], wireless sensor networks [5,10,13,53,63,72], stock price forecasting [17], intrusion detection and other security applications [2,35,45,51,61,62,66,71] and plant classification problem [18]. Metaheuristics algorithms have been also employed to tune the cloud, edge and fog computing [3,6,16,24,52,65], feature selection [9,20,23,36,41,54,67], dropout regularization [12], a wide spectrum of COVID-19 challenges [26,64,[68][69][70], artificial neural networks optimization [4,7,8,…”

Perspectives of RealSense and ZED Depth Sensors for Robotic Vision Applications

“…These are principal features related to all depth cameras since the lenses, aperture, and light significantly affect the image quality of any camera, not only the depth camera. These features enable high-quality depth images to be obtained, upon which promising robotic vision applications can be developed [41][42][43][44][45][46][47][48][49][50][51][52][53][54][55][56][57][58][59]. According to the Stereolabs [16], the main features of ZED cameras are listed in Table 2.…”

“…Up to now, 3D depth cameras have been very expensive, and their utilization has been complex and burdensome from a hardware standpoint. Nowadays, thanks to technical progress, the price of 3D depth sensors that can measure image depth is considerably affordable and their use much simpler [41][42][43][44][45][46][47][48][49][50][51][52][53][54][55][56][57][58][59].…”