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 …

An efficient combination of convolutional neural network and LightGBM algorithm for lung cancer histopathology classification

EAR Hamed, MAM Salem, NL Badr, MF Tolba - Diagnostics, 2023 - mdpi.com
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 …

Explainable synthetic image generation to improve risk assessment of rare pediatric heart transplant rejection

FO Giuste, R Sequeira, V Keerthipati, P Lais… - Journal of biomedical …, 2023 - Elsevier
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 …

Transformer-based semantic segmentation and CNN network for detection of histopathological lung cancer

LF Talib, J Amin, M Sharif, M Raza - Biomedical Signal Processing and …, 2024 - Elsevier
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 …

FAST: A Dual-tier Few-Shot Learning Paradigm for Whole Slide Image Classification

K Fu, X Luo, L Qu, S Wang, Y Xiong… - arXiv preprint arXiv …, 2024 - arxiv.org
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) …

FPGA implementation of deep learning architecture for kidney cancer detection from histopathological images

S Lal, AK Chanchal, J Kini, GK Upadhyay - Multimedia Tools and …, 2024 - Springer
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 …

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 metric learning for histopathological image classification

S Calderaro, GL Bosco, R Rizzo… - 2022 IEEE Eighth …, 2022 - ieeexplore.ieee.org
Neural networks demonstrated to be effective in multiple classification tasks with
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

O Katar, O Yildirim, RS Tan, UR Acharya - Diagnostics, 2024 - mdpi.com
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 …

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 …