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[HTML][HTML] Recent advancement in cancer detection using machine learning: Systematic survey of decades, comparisons and challenges
T Saba - Journal of infection and public health, 2020 - Elsevier
Cancer is a fatal illness often caused by genetic disorder aggregation and a variety of
pathological changes. Cancerous cells are abnormal areas often growing in any part of …
pathological changes. Cancerous cells are abnormal areas often growing in any part of …
Practical utility of liver segmentation methods in clinical surgeries and interventions
Clinical imaging (eg, magnetic resonance imaging and computed tomography) is a crucial
adjunct for clinicians, aiding in the diagnosis of diseases and planning of appropriate …
adjunct for clinicians, aiding in the diagnosis of diseases and planning of appropriate …
Dual cross-attention for medical image segmentation
Abstract We propose Dual Cross-Attention (DCA), a simple yet effective attention module
that enhances skip-connections in U-Net-based architectures for medical image …
that enhances skip-connections in U-Net-based architectures for medical image …
Microscopic brain tumor detection and classification using 3D CNN and feature selection architecture
Brain tumor is one of the most dreadful natures of cancer and caused a huge number of
deaths among kids and adults from the past few years. According to WHO standard, the …
deaths among kids and adults from the past few years. According to WHO standard, the …
Brain tumor segmentation using K‐means clustering and deep learning with synthetic data augmentation for classification
Image processing plays a major role in neurologists' clinical diagnosis in the medical field.
Several types of imagery are used for diagnostics, tumor segmentation, and classification …
Several types of imagery are used for diagnostics, tumor segmentation, and classification …
DC-UNet: rethinking the U-Net architecture with dual channel efficient CNN for medical image segmentation
Recently, deep learning has become much more popular in computer vision applications.
The Convolutional Neural Network (CNN) has brought a breakthrough in image …
The Convolutional Neural Network (CNN) has brought a breakthrough in image …
Classification of acute lymphoblastic leukemia using deep learning
Acute Leukemia is a life‐threatening disease common both in children and adults that can
lead to death if left untreated. Acute Lymphoblastic Leukemia (ALL) spreads out in children's …
lead to death if left untreated. Acute Lymphoblastic Leukemia (ALL) spreads out in children's …
Region extraction and classification of skin cancer: A heterogeneous framework of deep CNN features fusion and reduction
Cancer is one of the leading causes of deaths in the last two decades. It is either diagnosed
malignant or benign–depending upon the severity of the infection and the current stage. The …
malignant or benign–depending upon the severity of the infection and the current stage. The …
Deep learning based brain tumor detection and classification
NM Dipu, SA Shohan… - … International conference on …, 2021 - ieeexplore.ieee.org
One of the most crucial tasks of neurologists and radiologists is early brain tumor detection.
However, manually detecting and segmenting brain tumors from Magnetic Resonance …
However, manually detecting and segmenting brain tumors from Magnetic Resonance …
[HTML][HTML] An effective skin cancer classification mechanism via medical vision transformer
Skin Cancer (SC) is considered the deadliest disease in the world, killing thousands of
people every year. Early SC detection can increase the survival rate for patients up to 70 …
people every year. Early SC detection can increase the survival rate for patients up to 70 …