FastNet: A Lightweight Convolutional Neural Network for Tumors Fast Identification in Mobile-Computer-Assisted Devices

Abstract

Histopathology diagnosis is an important standard for breast tumors identifying. However, histopathology image analysis is complex, tedious and error-prone, due to the superresolution image. In recent years, deep learning technology has been successfully applied to histopathology image analysis and made great progress. The well-known deep neural networks usually have tens of million parameters, which consume much memory to deploy the state-of-the-art model. In addition, deep neural networks rely on high-performance hardware resources, which impede the deployment of state-of-the-art model on portable equipment. In this work, a novel framework which consists of a weight accumulation method and a lightweight fast neural network (FastNet) was proposed for tumor fast identification (TFI) in mobile computer-assisted devices. The weight accumulation method was designed to obtain the tissue mask regions of interest and remove the useless background area in histopathology images, which greatly reduces the redundant computation cost. Furthermore, we proposed the lightweight FastNet to improve the computational efficiency on mobile devices. A novel attention loss function was designed and applied in FastNet. The attention loss function pays more attention on the positive samples and the indistinguishable samples, which greatly improves performance. The proposed FastNet was compared with three state-of-the-art methods commonly used for image classification and object detection. Experimental results indicated that FastNet achieves highest recall of 96.94%, highest F1 score of 97.33% and highest accuracy of 97.34%, besides least trainable parameters of 0.22M and smallest floating point operations of 210M FLOPs.

Defense Strategies for Epidemic Cyber Security Threats: Modeling and Analysis by Using a Machine Learning Approach

“…The proposed model is a compartmental SEIAR model, which is an acronym that stands for Susceptible, Exposed, Infectious, Asymptomatic, and Recovered, as depicted in Figure (2). Table (17) describes the notation for the SEIAR model.…”

“…In this compartmental SEIAR model, I is infectious devices and the addition of infectious devices is represented by A. They may be considered to have a reduced βλ transmission rate when it comes to being infectious, as evidenced by the results shown in Figure (2). A dashed line in the same illustration serves as a representation of this.…”

“…After losing their temporal susceptibility, recovered devices nally return at an η rate towards the unprotected compartment. The corresponding system of ordinary dierential equations represents the mathematical dynamic model of the preceding Figure (2):…”

“…Without technology, this is nearly impossible since there will be a lot of delay. Technology has a signicant role in increasing eciency [2]. This means that people in an industry must be technologically prepared and accomplish their responsibilities signicantly more quickly.…”

Digestive tract bubble recognition model based on ViT

“…This core component encompasses the optical imaging system and illumination fibers, where the signals collected by the optical imaging system are transmitted to an image sensor, transforming optical signals into electrical ones. Next, the electrical signals are transmitted to the endoscope's image processor, undergoing a series of image processing algorithms [2]. Finally, the processed signals are displayed on a monitor at the terminal.…”