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Showing total 4 results
4 results

1. New Brain Cancer Data Have Been Reported by Investigators at Mohamed Sathak Engineering College (Enhancing Image Classification Using Adaptive Convolutional Autoencoder-based Snow Avalanches Algorithm).

Subjects
IMAGE recognition (Computer vision), BRAIN cancer, ENGINEERING schools, ALGORITHMS, VIDEO processing
Abstract

A recent study conducted at Mohamed Sathak Engineering College in Tamil Nadu, India, focused on the detection and classification of brain tumors using magnetic resonance imaging (MRI). The researchers proposed an algorithm called Adaptive Convolutional Autoencoder-based Snow Avalanches (ACAE-SA) to automate the detection and categorization of brain tumors from MRI images. The algorithm was found to be effective and outperformed existing techniques. This research has been peer-reviewed and published in the journal Signal Image and Video Processing. For more information, readers can contact the researchers at Mohamed Sathak Engineering College. [Extracted from the article]

Published
2024

2. New Breast Cancer Data Have Been Reported by Investigators at School of Computing (Swarm Intelligent Metaheuristic Optimization Algorithms-based Artificial Neural Network Models for Breast Cancer Diagnosis: Emerging Trends, Challenges and...).

Subjects
ARTIFICIAL neural networks, METAHEURISTIC algorithms, CANCER diagnosis, BREAST cancer, OPTIMIZATION algorithms
Abstract

A recent report from investigators at the School of Computing in Tamil Nadu, India, discusses the use of swarm intelligent metaheuristic optimization algorithms-based artificial neural network models for breast cancer diagnosis. The report emphasizes the importance of early detection and the use of optimization algorithms to improve accuracy and efficiency in predicting breast cancer. The researchers compare different approaches and highlight the significance of swarm intelligent algorithms-based optimized artificial neural network models. The report also discusses evaluation metrics and future research directions in this field. This research has been peer-reviewed and provides valuable insights into breast cancer diagnosis. [Extracted from the article]

Published
2024

3. Reports from College of Engineering and Technology Highlight Recent Findings in Cancer Detection (Comparative Study and Analysis On Skin Cancer Detection Using Machine Learning and Deep Learning Algorithms).

Subjects
SKIN cancer, MACHINE learning, DEEP learning, EARLY detection of cancer, ENGINEERING schools, CONVOLUTIONAL neural networks
Abstract

Tamil Nadu, India, Asia, Algorithms, Cancer, Cancer Detection, Cyborgs, Emerging Technologies, Health and Medicine, Machine Learning, Oncology, Skin Cancer, Skin Neoplasms Keywords: Tamil Nadu; India; Asia; Algorithms; Cancer; Cancer Detection; Cyborgs; Emerging Technologies; Health and Medicine; Machine Learning; Oncology; Skin Cancer; Skin Neoplasms EN Tamil Nadu India Asia Algorithms Cancer Cancer Detection Cyborgs Emerging Technologies Health and Medicine Machine Learning Oncology Skin Cancer Skin Neoplasms 732 732 1 09/19/23 20230919 NES 230919 2023 SEP 19 (NewsRx) -- By a News Reporter-Staff News Editor at Clinical Oncology Week -- Investigators publish new report on Oncology - Cancer Detection. [Extracted from the article]

Published
2023

4. Recent Findings from School of Electrical Engineering (SENSE) Has Provided New Information about Cancer (Machine Learning and Computer Vision Based Methods for Cancer Classification: a Systematic Review).

Subjects
COMPUTER vision, TUMOR classification, ELECTRICAL engineering, MACHINE learning, ENGINEERING schools
Abstract

A recent report from the School of Electrical Engineering in Tamil Nadu, India discusses the use of machine learning and computer vision in cancer classification. The researchers highlight the potential of these technologies in speeding up the diagnosis of cancer and improving treatment planning. The report focuses on nine major cancer types and examines the use of various imaging modalities and machine learning algorithms in their classification. The objective of the study is to provide researchers and medical experts with a comprehensive understanding of the current state of cancer classification using computer vision and machine learning, with the aim of enhancing cancer detection and therapy precision. [Extracted from the article]

Published
2024