Classification of non-small cell lung cancer using one-dimensional convolutional neural network
Abstract Non-Small Cell Lung Cancer (NSCLC) is a major lung cancer type. Proper
diagnosis depends mainly on tumor staging and grading. Pathological prognosis often faces …
diagnosis depends mainly on tumor staging and grading. Pathological prognosis often faces …
Automated AJCC staging of non-small cell lung cancer (NSCLC) using deep convolutional neural network (CNN) and recurrent neural network (RNN)
Purpose A large chunk of lung cancers are of the type non-small cell lung cancer (NSCLC).
Both the treatment planning and patients' prognosis depend greatly on factors like AJCC …
Both the treatment planning and patients' prognosis depend greatly on factors like AJCC …
An efficient content-based image retrieval system for the diagnosis of lung diseases
The main problem in content-based image retrieval (CBIR) systems is the semantic gap
which needs to be reduced for efficient retrieval. The common imaging signs (CISs) which …
which needs to be reduced for efficient retrieval. The common imaging signs (CISs) which …
Prediction of non-small cell lung cancer histology by a deep ensemble of convolutional and bidirectional recurrent neural network
D Moitra, RK Mandal - Journal of Digital Imaging, 2020 - Springer
Histology subtype prediction is a major task for grading non-small cell lung cancer (NSCLC)
tumors. Invasive methods such as biopsy often lack in tumor sample, and as a result …
tumors. Invasive methods such as biopsy often lack in tumor sample, and as a result …
Automated grading of non-small cell lung cancer by fuzzy rough nearest neighbour method
Lung cancer is one of the most lethal diseases across the world. Most lung cancers belong
to the category of non-small cell lung cancer (NSCLC). Many studies have so far been …
to the category of non-small cell lung cancer (NSCLC). Many studies have so far been …
[PDF][PDF] Comparison of multimodal tumor image segmentation techniques
D Moitra - Int J Adv Res Comput Sci, 2018 - researchgate.net
Use of multimodal imaging for the classification of tumors in human body is on the rise.
Segmentation is an important step of such classification process. There is need of carrying …
Segmentation is an important step of such classification process. There is need of carrying …
Classification of malignant tumors by a non-sequential recurrent ensemble of deep neural network model
Many significant efforts have so far been made to classify malignant tumors by using various
machine learning methods. Most of the studies have considered a particular tumor genre …
machine learning methods. Most of the studies have considered a particular tumor genre …
American Joint Committee on Cancer staging of lung and renal cancers using a recurrent deep neural network model
D Moitra - Artificial Intelligence in Cancer Diagnosis and …, 2022 - iopscience.iop.org
In this study, a new deep learning model has been developed by combining convolutional
and recurrent layers. Its aim is to automate the overall American Joint Committee on Cancer …
and recurrent layers. Its aim is to automate the overall American Joint Committee on Cancer …
Deep learning model for prediction of malignant tumors in human body with special reference to multimodel imaging techniques
D Moitra - 2020 - ir.nbu.ac.in
Several attempts have so far been made to detect malignant tumors by combining
biomedical imaging and machine learning techniques. The machine learning methods …
biomedical imaging and machine learning techniques. The machine learning methods …