A Review on Breast Cancer Prediction Using Machine Learning and Deep Learning Techniques

2024-09-04T09:48:42+00:00

Breast cancer is one of the most prevalent and chronic disease that affect women. To overcome this disease, effective medical treatment is required. Early detection of the disease plays an important role for suitable medication and survival of patient. To identify the breast cancer in the patients, standard imaging technique mammography is used. Due to the subtle and varied nature of cancer tissues interpreting mammogram images can be a challenge to doctors. Machine learning (ML) and Deep Learning (DL) techniques offer promising solutions that provide efficient breast cancer detection from mammograms. In this review paper a comprehensive review of ML and DL algorithms and their applications in mammogram image analysis are presented. Various supervised and unsupervised learning techniques, such as convolutional neural networks (CNNs), support vector machines (SVMs), random forests, and other popular ML and DL models are discussed in paper. The integration of these DL methods that are efficiently used in image preprocessing techniques, feature extraction, and classification strategies. The overall survey focusses on various performance metrics, datasets, and benchmarks used in existing studies. Further the strengths and limitations of different approaches used by various researchers are identified. By understating current research trends this paper aims to contribute to the ongoing development of more accurate and reliable breast cancer detection systems using advanced ML techniques.

ADVANCED AUTONOMOUS SURVEILLANCE ROBOT FOR ENHANCED MONITORING AND INDIVIDUAL IDENTIFICATION

2024-08-01T17:18:10+00:00

The primary objective is to detect and identify suspicious activities and potential threats in a precise manner while prioritizing human safety leveraging surveillance technology and machine learning. The implementation of this system involves coding in Python using the OpenCV library. It utilizes Wi-Fi connectivity as a means of communication. The robot is equipped with a Raspberry Pi along with a USB web camera, which captures video footage and employs object detection algorithms to identify unknown individuals. When a person or an object is detected, the system sends an email to the dedicated email addresses including an image of the unrecognized individual. The proposed system is designed as a unified unit responsible for monitoring the environment for hazardous conditions and delivering real-time video feedback. The proposed system is simulated and tested in real-time to observe its functionality, and it is observed that the system works properly as per given input conditions.

Turkish Journal of Computer and Mathematics Education (TURCOMAT)

A Review on Breast Cancer Prediction Using Machine Learning and Deep Learning Techniques

ADVANCED AUTONOMOUS SURVEILLANCE ROBOT FOR ENHANCED MONITORING AND INDIVIDUAL IDENTIFICATION