[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 …
Facial emotion recognition using conventional machine learning and deep learning methods: current achievements, analysis and remaining challenges
AR Khan - Information, 2022 - mdpi.com
Facial emotion recognition (FER) is an emerging and significant research area in the pattern
recognition domain. In daily life, the role of non-verbal communication is significant, and in …
recognition domain. In daily life, the role of non-verbal communication is significant, and in …
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 …
Brain tumor detection and multi‐classification using advanced deep learning techniques
A brain tumor is an uncontrolled development of brain cells in brain cancer if not detected at
an early stage. Early brain tumor diagnosis plays a crucial role in treatment planning and …
an early stage. Early brain tumor diagnosis plays a crucial role in treatment planning and …
Attention 3D U-Net with Multiple Skip Connections for Segmentation of Brain Tumor Images
Among researchers using traditional and new machine learning and deep learning
techniques, 2D medical image segmentation models are popular. Additionally, 3D …
techniques, 2D medical image segmentation models are popular. Additionally, 3D …
Computer vision for microscopic skin cancer diagnosis using handcrafted and non‐handcrafted features
T Saba - Microscopy Research and Technique, 2021 - Wiley Online Library
Skin covers the entire body and is the largest organ. Skin cancer is one of the most dreadful
cancers that is primarily triggered by sensitivity to ultraviolet rays from the sun. However, the …
cancers that is primarily triggered by sensitivity to ultraviolet rays from the sun. However, the …
Brain image segmentation in recent years: A narrative review
Brain image segmentation is one of the most time-consuming and challenging procedures in
a clinical environment. Recently, a drastic increase in the number of brain disorders has …
a clinical environment. Recently, a drastic increase in the number of brain disorders has …
Artificial intelligence in brain tumor detection through MRI scans: advancements and challenges
A brain tumor is one of the most perilous diseases in human beings. The manual
segmentation of brain tumors is costly and takes a lot of time; due to this reason, automated …
segmentation of brain tumors is costly and takes a lot of time; due to this reason, automated …
Breast microscopic cancer segmentation and classification using unique 4‐qubit‐quantum model
J Amin, M Sharif, SL Fernandes… - Microscopy …, 2022 - Wiley Online Library
The visual inspection of histopathological samples is the benchmark for detecting breast
cancer, but a strenuous and complicated process takes a long time of the pathologist …
cancer, but a strenuous and complicated process takes a long time of the pathologist …