A comprehensive analysis of recent advancements in cancer detection using machine learning and deep learning models for improved diagnostics
Purpose There are millions of people who lose their life due to several types of fatal
diseases. Cancer is one of the most fatal diseases which may be due to obesity, alcohol …
diseases. Cancer is one of the most fatal diseases which may be due to obesity, alcohol …
[HTML][HTML] Recent outcomes and challenges of artificial intelligence, machine learning and deep learning applications in neurosurgery–Review applications of artificial …
Neurosurgeons receive extensive technical training, which equips them with the knowledge
and skills to specialise in various fields and manage the massive amounts of information …
and skills to specialise in various fields and manage the massive amounts of information …
Improving brain tumor classification: an approach integrating pre-trained CNN models and machine learning algorithms
Accurate detection of brain tumors is crucial for enhancing patient outcomes, yet the
interpretation of Magnetic Resonance Imaging (MRI) scans poses significant challenges …
interpretation of Magnetic Resonance Imaging (MRI) scans poses significant challenges …
Computer-aided diagnosis using embedded ensemble deep learning for multiclass drug-resistant tuberculosis classification
Introduction This study aims to develop a web application, TB-DRD-CXR, for the
categorization of tuberculosis (TB) patients into subgroups based on their level of drug …
categorization of tuberculosis (TB) patients into subgroups based on their level of drug …
DEF-SwinE2NET: Dual enhanced features guided with multi-model fusion for brain tumor classification using preprocessing optimization
Brain tumors exhibit significant variability in shape, size, and location, making it difficult to
achieve consistent and accurate classification. It requires advanced algorithms for handling …
achieve consistent and accurate classification. It requires advanced algorithms for handling …
Optimized attention-based lightweight CNN using particle swarm optimization for brain tumor classification
Timely detection of brain tumors is crucial for develo** effective treatment strategies and
improving the overall well-being of patients. We introduced an innovative approach in this …
improving the overall well-being of patients. We introduced an innovative approach in this …
ChatGPT-powered deep learning: elevating brain tumor detection in MRI scans
Purpose Accurate diagnosis of brain tumors is crucial for effective treatment and improved
patient outcomes. Magnetic resonance imaging (MRI) is a common method for detecting …
patient outcomes. Magnetic resonance imaging (MRI) is a common method for detecting …
Deep learning approaches for brain tumor detection and classification using mri images (2020 to 2024): A systematic review
Brain tumor is a type of disease caused by uncontrolled cell proliferation in the brain leading
to serious health issues such as memory loss and motor impairment. Therefore, early …
to serious health issues such as memory loss and motor impairment. Therefore, early …
[HTML][HTML] FlexiCombFE: A flexible, combination-based feature engineering framework for brain tumor detection
Deep learning models are prevalently employed for image classification, but significant
opportunities remain within the domain of feature engineering. This study introduces …
opportunities remain within the domain of feature engineering. This study introduces …
Transformative Advances in AI for Precise Cancer Detection: A Comprehensive Review of Non-Invasive Techniques
Cancer continues to be a primary cause of death worldwide, highlighting the critical need for
early diagnosis methods. Automated, quick, and efficient technologies are critical to this …
early diagnosis methods. Automated, quick, and efficient technologies are critical to this …