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[HTML][HTML] A systematic review of deep learning data augmentation in medical imaging: Recent advances and future research directions
Data augmentation involves artificially expanding a dataset by applying various
transformations to the existing data. Recent developments in deep learning have advanced …
transformations to the existing data. Recent developments in deep learning have advanced …
Enhancing Brain Tumor Diagnosis: A Comparative Review of Systems with and without eXplainable AI
Deep Learning (DL) and computer vision may be used to distinguish between various
anatomical features in the human body. As technology has developed, several DL methods …
anatomical features in the human body. As technology has developed, several DL methods …
Optimized brain tumor identification via graph sample and aggregate-attention network with Artificial Lizard Search Algorithm
A brain tumour is an abnormal growth of brain nerves that interferes with normal brain
function. It causes a great deal of deaths. Timely detection and treatment are essential for …
function. It causes a great deal of deaths. Timely detection and treatment are essential for …
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 …
EfficientNetV2 based for MRI brain tumor image classification
An accurate and timely diagnosis is of utmost importance when it comes to treating brain
tumors effectively. To facilitate this process, we have developed a brain tumor classification …
tumors effectively. To facilitate this process, we have developed a brain tumor classification …
Deep Learning-Based Waste Classification with Transfer Learning Using EfficientNet-B0 Model
Recycling of waste is a significant challenge in modern waste management. Conventional
techniques that use inductive and capacitive proximity sensors exhibit limitations in accuracy …
techniques that use inductive and capacitive proximity sensors exhibit limitations in accuracy …
A Comparative Analysis on Machine Learning based Brain Tumor Detection Techniques
It has been observed that finding an anomaly, like a fracture in a bone or unwanted growth
in an organ of human being, has been easier since the advent of advanced technologies …
in an organ of human being, has been easier since the advent of advanced technologies …
EfficientNet-B7 framework for anomaly detection in mammogram images
At present, handling imbalanced data, deciphering complex data patterns, selecting suitable
unsupervised learning algorithms, and ensuring computational efficiency are among the …
unsupervised learning algorithms, and ensuring computational efficiency are among the …
Regression modeling with convolutional neural network for predicting extent of resection from preoperative MRI in giant pituitary adenomas: a pilot study
OBJECTIVE Giant pituitary adenomas (GPAs) are challenging skull base tumors due to their
size and proximity to critical neurovascular structures. Achieving gross-total resection (GTR) …
size and proximity to critical neurovascular structures. Achieving gross-total resection (GTR) …