Advancements in AI for Oncology: Developing an Enhanced YOLOv5-based Cancer Cell Detection System

Abstract

As artificial intelligence (AI) theory becomes more sophisticated and its utilization spreads across daily life, education, and professional settings, the adoption of AI for medical diagnostic and service purposes stands as a logical progression in the evolution of medical technologies. This document outlines a novel approach to detecting cancer cell targets using a deep learning-based system, marking a critical step towards integrating AI into cancer diagnostics. The process of detecting cancer cell targets entails the localization of cell types within images of cells. By capitalizing on the strengths of the YOLOv5 model—a deep learning-driven, end-to-end, real-time object detection framework known for its efficiency, superior performance, adaptability, and user-friendly PyTorch integration—this research presents an enhanced YOLOv5 model incorporating both a feature pyramid network and the original YOLOv5 architecture. The ultimate aim is to facilitate precise detection of targets in cancer cell images. The experimental data demonstrate the system's negligible error rate in detection, swift processing capabilities, and exceptional reliability.

Research on Intelligent System of Multimodal Deep Learning in Image Recognition

“…The output plane is , where . The subsampling layer is smoothed by on each access plane (2) "Softmax" layer is introduced to explain these vectors, and the calculation formula is as follows (3) (4) After optimization, the parameter θ is trained by the stochastic gradient descent algorithm, and the gradient of the random sample is calculated, and then updated: (5) In the process of dim dim target recognition, this method uses the fixed features of continuous and non-continuous frames to optimize the recognition method, so as to ensure the high accuracy of the recognition results.…”

“…In recent years, with the development of deep learning technology, it has made important breakthroughs in many fields [3][4]. For example, convolutional neural networks (CNNs) perform well with medical recognition but encounter limitations in computing power, network depth, and optimization algorithms when applied to other types of recognition [5] [6]. Another study that used an 8-layer deep CNN for image recognition reported good results, leading to further applications in image processing [7].…”

Research on the Application of Deep Learning in Medical Image Segmentation and 3D Reconstruction

“…Compared with traditional methods, DL model can automatically learn the feature representation in medical images, thus achieving more accurate and robust segmentation and reconstruction results [1][2]. For example, the network architecture specially designed for MIS such as U-Net has become one of the standard tools in medical imaging [3].…”

Research on Image Classification And Semantic Segmentation Model Based on Convolutional Neural Network

“…By integrating the basic theory of CNN, the latest architectural progress, and its practical application in two types of tasks, we aim to build an innovative model framework that combines the dual advantages of advanced feature extraction and pixel-level accurate positioning. The goal of this research is not only to contribute new theoretical insights and practical methods to image analysis technology, but also to promote these advanced technologies to cross traditional boundaries and achieve wide application in emerging fields such as medical Detection [13][14][15], geography [16][17], and prediction [18][19], where deep learning is already widely used. This approach aims to drive the continuous expansion and innovation of computer vision technology.…”