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Explaining Deep Features Using Radiologist-Defined Semantic Features and Traditional Quantitative Features

2019 Tomography  
By training with the use of different types of images, the CNN learns to recognize various patterns and textures.  ...  These features give us information about a nodule, for example, nodule size, pixel intensity, histogram-based information, and texture information from wavelets or a convolution kernel.  ...  Traditional quantitative features consist of tumor size, tumor shape, Law's texture features, tumor location, etc.  ... 
doi:10.18383/j.tom.2018.00034 pmid:30854457 pmcid:PMC6403047 fatcat:hrys5hgcnrdmnev5ex3qbcieby

Detection of Lung Cancer Lesions Using 3D Convolutional Neural Networks and Segmentation for Accurate Detection

Rajani Kumari, C. Thanuja, K. Sai Thanvi, K. Lakshmi, U. Lavanya
2021 International Journal of Scientific Research in Science and Technology  
categories: with cancerous lung nodules and without lung nodules.  ...  In existing method, the candidate ROIs shape features are calculated, and some blood vessels are get rid of using rule-based according to shape features; secondly, the remainder candidates gray and texture  ...  "Lung nodule classification combining rule-based and SVM."  ... 
doi:10.32628/ijsrst218442 fatcat:2bnl4ntb3fe7tmx5klhpivrjei

Radiomics and artificial intelligence in lung cancer screening

Franciszek Binczyk, Wojciech Prazuch, Paweł Bozek, Joanna Polanska
2021 Translational Lung Cancer Research  
This paper illuminates the evolution and current state of artificial intelligence methods in lung imaging and the detection and diagnosis of pulmonary nodules, with a particular emphasis on radiomics and  ...  and construction of the classification model.  ...  (85) designed Deep 3D Dual Path Nets (3D DPN26) with 3D Faster R-CNN for nodule detection with 3D dual paths and a U-net-like structure for feature extraction.  ... 
doi:10.21037/tlcr-20-708 pmid:33718055 pmcid:PMC7947422 fatcat:qiqnjpiafzfhxeicqlol4v6po4

An Efficient Method for Detection and Classification of Pulmonary Neoplasm based on Deep Learning Technique

Venkatesh C, SivaYamini L
2021 Helix  
Cancers of the lung and pancreas are two of the most frequent cancers.  ...  CNN segments the tumor part if the tumor in the image is assessed as malignant.  ...  Fig 6 : 6 Fig 6: Preprocessing of Raw data Classification U-Net is a Convolution Neural Networks (CNN)-based method. In the identified output image, this is used for tumor segmentation.  ... 
doi:10.29042/2021-11-1-6-12 fatcat:pfd4rbw5nrg55ha6hdbpy2n3nq

Predicting Lung Nodule Malignancies by Combining Deep Convolutional Neural Network and Handcrafted Features [article]

Shulong Li, Panpan Xu, Bin Li, Liyuan Chen, Zhiguo Zhou, Hongxia Hao, Yingying Duan, Michael Folkert, Jianhua Ma, Steve Jiang, Jing Wang
2018 arXiv   pre-print
To predict lung nodule malignancy with a high sensitivity and specificity, we propose a fusion algorithm that combines handcrafted features (HF) into the features learned at the output layer of a 3D deep  ...  We then trained 3D CNNs modified from three state-of-the-art 2D CNN architectures (AlexNet, VGG-16 Net and Multi-crop Net) to extract the CNN features learned at the output layer.  ...  Acknowledgement This work was partly supported by the American Cancer Society (ACS-IRG-02-196), the US National Institutes of Health (5P30CA142543), and the National Natural Science Foundation of China  ... 
arXiv:1809.02333v2 fatcat:pwknhq7i2nc6rbeqvnxvhg5wu4

Comparison of machine learning methods for classifying mediastinal lymph node metastasis of non-small cell lung cancer from 18F-FDG PET/CT images

Hongkai Wang, Zongwei Zhou, Yingci Li, Zhonghua Chen, Peiou Lu, Wenzhi Wang, Wanyu Liu, Lijuan Yu
2017 EJNMMI Research  
However, CNN does not make use of the import diagnostic features, which have been proved more discriminative than the texture features for classifying small-sized lymph nodes.  ...  Because CNN does not need tumor segmentation or feature calculation, it is more convenient and more objective than the classical methods.  ...  CNN also avoids using the debated texture features which are affected by tumor size.  ... 
doi:10.1186/s13550-017-0260-9 pmid:28130689 pmcid:PMC5272853 fatcat:3uug5wfc6rgjlcbpuqfenn2zqu

Recent advancement in cancer detection using machine learning: Systematic survey of decades, comparisons and challenges

Tanzila Saba
2020 Journal of Infection and Public Health  
The aim of the research is to analyze, review, categorize and address the current developments of human body cancer detection using machine learning techniques for breast, brain, lung, liver, skin cancer  ...  Cancer also known as tumor must be quickly and correctly detected in the initial stage to identify what might be beneficial for its cure.  ...  Acknowledgements This work was supported by the research Project [Brain Tumor Detection and Classification using 3D CNN and Feature Selection Architecture]; Prince Sultan University; Saudi Arabia [SEED-CCIS  ... 
doi:10.1016/j.jiph.2020.06.033 pmid:32758393 fatcat:sglazth4znh5jjtozguaktruce

Deep fusion of gray level co-occurrence matrices for lung nodule classification [article]

Ahmed Saihood, Hossein Karshenas, AhmadReza Naghsh Nilchi
2022 arXiv   pre-print
Extended experiments are run to assess this fusion structure by considering 2D-GLCM computations based 2D-slices fusion, and an approximation of this 3D-GLCM with volumetric 2.5D-GLCM computations-based  ...  The yield of the same are 98.7%, 98%, and 99%, for the 3D-GLCM fusion.  ...  The potential and limitations of CNN-based image classification models in discriminating lung tumors are highlighted.  ... 
arXiv:2205.05123v1 fatcat:mpgnycr7mnddpju6o7df2eknh4

Medical Image Analysis using Convolutional Neural Networks: A Review [article]

Adnan Qayyum, Syed Muhammad Anwar, Muhammad Majid, Muhammad Awais, Majdi Alnowami
2017 arXiv   pre-print
The application area covers the whole spectrum of medical image analysis including detection, segmentation, classification, and computer aided diagnosis.  ...  This paper presents a review of the state-of-the-art convolutional neural network based techniques used for medical image analysis.  ...  An adaptive CADx system for classification of breast tumors based on tumor sizes in screening ultrasound is presented in [53] .  ... 
arXiv:1709.02250v1 fatcat:mlk3vdn7ibggvcxzsb7c23kibq

A Novel Nodule Edge Sharpness Radiomic Biomarker Improves Performance of Lung-RADS for Distinguishing Adenocarcinomas from Granulomas on Non-Contrast CT Scans

Mehdi Alilou, Prateek Prasanna, Kaustav Bera, Amit Gupta, Prabhakar Rajiah, Michael Yang, Frank Jacono, Vamsidhar Velcheti, Robert Gilkeson, Philip Linden, Anant Madabhushi
2021 Cancers  
In conjunction with the LDA classifier, NIS radiomics classified nodules with an AUC of 0.82 ± 0.04, 0.77, and 0.71 respectively on St, Sv, and Siv.  ...  The aim of this study is to evaluate whether NIS radiomics can distinguish lung adenocarcinomas from granulomas on non-contrast CT scans, and also to improve the performance of Lung-RADS by reclassifying  ...  The 3D model also used 3D patches with a receptive field size of 50 pixels in the XY plane and 10 slices in the Z plane (50×50×10).  ... 
doi:10.3390/cancers13112781 fatcat:s6ivimx44rcjxi4hc5ee6bw4ou

Predicting Distant Metastases in Soft-Tissue Sarcomas from PET-CT scans using Constrained Hierarchical Multi-Modality Feature Learning [article]

Yige Peng, Lei Bi, Ashnil Kumar, Michael Fulham, Dagan Feng, Jinman Kim
2021 arXiv   pre-print
The state-of-the-art in radiomics is based on convolutional neural networks (CNNs).  ...  This approach, however, may not be scalable to tumors with complex boundaries and where there are multiple other sites of disease.  ...  In contrast, 2D CNNs based methods (e.g., 2D-CNN-CT and 2D-CNN-PET) have limited representation capability of tumor characteristics in two dimensions with few axial slices.  ... 
arXiv:2104.11416v1 fatcat:5rxmrmx7wrbkrec5yx7xsrn6ii

A deep learning-facilitated radiomics solution for the prediction of lung lesion shrinkage in non-small cell lung cancer trials [article]

Antong Chen, Jennifer Saouaf, Bo Zhou, Randolph Crawford, Jianda Yuan, Junshui Ma, Richard Baumgartner, Shubing Wang, Gregory Goldmacher
2020 arXiv   pre-print
The approach starts with the classification of lung lesions from the set of primary and metastatic lesions at various anatomic locations.  ...  Herein we propose a deep learning-based approach for the prediction of lung lesion response based on radiomic features extracted from clinical CT scans of patients in non-small cell lung cancer trials.  ...  Since the goal of the study is to predict lesion size change, 15 features directly or indirectly related to the lesion's size in 3D are eliminated, leaving 93 features, including 3 shape-based features  ... 
arXiv:2003.02943v1 fatcat:z2gxpavqonednoctdnw7vv2tyu

Bone Cancer Detection Using Feature Extraction with Classification Using K-Nearest Neighbor and Decision Tree Algorithm [chapter]

Satheesh Kumar, B, Sathiyaprasad. B
2021 Advances in Parallel Computing  
The first stage is extracting the feature of segmented bone image using Gray-Level Co-occurrence Matrix (GLCM) method is applied to extract the features in terms of statistical texture-based and the second  ...  phase is classification of extracted feature using K-NN with decision tree algorithm.  ...  Finally, when compared to prevailing 3D-GLCM CNN, CNN with gene data, and RSS-BW proposed KNN-DA algorithm, shows better results. 5.  ... 
doi:10.3233/apc210064 fatcat:gwu3y7iwfzhh7dhhuc2qdfwkue

AI-Based Detection, Classification and Prediction/Prognosis in Medical Imaging: Towards Radiophenomics [article]

Fereshteh Yousefirizi, Pierre Decazes, Amine Amyar, Su Ruan, Babak Saboury, Arman Rahmim
2022 arXiv   pre-print
This work reviews AI-based techniques, with a special focus on oncological PET and PET/CT imaging, for different detection, classification, and prediction/prognosis tasks.  ...  There is a spectrum of tumor histologies from benign to malignant that can be identified by AI-based classification approaches using image features.  ...  Acknowledgements This project was in part supported by the Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Grant RGPIN-2019-06467, and the Canadian Institutes of Health Research  ... 
arXiv:2110.10332v4 fatcat:vmpxhoolarbrve5ddyfn5umfim

An Appraisal of Lung Nodules Automatic Classification Algorithms for CT Images

Xinqi Wang, Keming Mao, Lizhe Wang, Peiyi Yang, Duo Lu, Ping He
2019 Sensors  
Our work reveals that lung nodule classification based on deep learning becomes dominant for its excellent performance.  ...  The paper aims to draw a systematic review of the state of the art of automatic classification of lung nodules.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/s19010194 fatcat:l22h2tvy6fhkxmjidrvci3dq3i
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