Deep learning techniques to diagnose lung cancer
L Wang - Cancers, 2022 - mdpi.com
Simple Summary This study investigates the latest achievements, challenges, and future
research directions of deep learning techniques for lung cancer and pulmonary nodule …
research directions of deep learning techniques for lung cancer and pulmonary nodule …
An efficient combination of convolutional neural network and LightGBM algorithm for lung cancer histopathology classification
The most dangerous disease in recent decades is lung cancer. The most accurate method of
cancer diagnosis, according to research, is through the use of histopathological images that …
cancer diagnosis, according to research, is through the use of histopathological images that …
Explainable synthetic image generation to improve risk assessment of rare pediatric heart transplant rejection
Expert microscopic analysis of cells obtained from frequent heart biopsies is vital for early
detection of pediatric heart transplant rejection to prevent heart failure. Detection of this rare …
detection of pediatric heart transplant rejection to prevent heart failure. Detection of this rare …
Transformer-based semantic segmentation and CNN network for detection of histopathological lung cancer
The lungs are a very important organ in a human. Any abnormality in the lungs ultimately
affects the whole body. Pulmonary nodules-initiated lung cancer that is very small in size …
affects the whole body. Pulmonary nodules-initiated lung cancer that is very small in size …
FAST: A Dual-tier Few-Shot Learning Paradigm for Whole Slide Image Classification
The expensive fine-grained annotation and data scarcity have become the primary
obstacles for the widespread adoption of deep learning-based Whole Slide Images (WSI) …
obstacles for the widespread adoption of deep learning-based Whole Slide Images (WSI) …
FPGA implementation of deep learning architecture for kidney cancer detection from histopathological images
Kidney cancer is the most common type of cancer, and designing an automated system to
accurately classify the cancer grade is of paramount importance for a better prognosis of the …
accurately classify the cancer grade is of paramount importance for a better prognosis of the …
Lung Cancer Detection Model Using Deep Learning Technique
AR Wahab Sait - Applied Sciences, 2023 - mdpi.com
Globally, lung cancer (LC) is the primary factor for the highest cancer-related mortality rate.
Deep learning (DL)-based medical image analysis plays a crucial role in LC detection and …
Deep learning (DL)-based medical image analysis plays a crucial role in LC detection and …
Deep metric learning for histopathological image classification
Neural networks demonstrated to be effective in multiple classification tasks with
performances that are similar to human capabilities. Notwithstanding, the viability of the …
performances that are similar to human capabilities. Notwithstanding, the viability of the …
[HTML][HTML] A Novel Hybrid Model for Automatic Non-Small Cell Lung Cancer Classification Using Histopathological Images
Background/Objectives: Despite recent advances in research, cancer remains a significant
public health concern and a leading cause of death. Among all cancer types, lung cancer is …
public health concern and a leading cause of death. Among all cancer types, lung cancer is …
Smart Diagnosis of Adenocarcinoma Using Convolution Neural Networks and Support Vector Machines
B Ananthakrishnan, A Shaik, S Chakrabarti, V Shukla… - Sustainability, 2023 - mdpi.com
Adenocarcinoma is a type of cancer that develops in the glands present on the lining of the
organs in the human body. It is found that histopathological images, obtained as a result of …
organs in the human body. It is found that histopathological images, obtained as a result of …