Two Ensemble-CNN Approaches for Colorectal Cancer Tissue Type Classification

Paladini, Emanuela; Vantaggiato, Edoardo; Bougourzi, Fares; Distante, Cosimo; Hadid, Abdenour; Taleb‐Ahmed, Abdelmalik

doi:10.3390/jimaging7030051

Cited by 38 publications

(31 citation statements)

References 37 publications

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“…Two classic models, ResNet-34 and Inception V3, were compared. With the advantage of residual learning, ResNet has performed well in many medical image-recognition tasks [ 37 , 38 ]. In this work, a 34-layer ResNet was built.…”

Section: Methodsmentioning

confidence: 99%

MVI-Mind: A Novel Deep-Learning Strategy Using Computed Tomography (CT)-Based Radiomics for End-to-End High Efficiency Prediction of Microvascular Invasion in Hepatocellular Carcinoma

Wang

et al. 2022

Cancers

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Microvascular invasion (MVI) in hepatocellular carcinoma (HCC) directly affects a patient’s prognosis. The development of preoperative noninvasive diagnostic methods is significant for guiding optimal treatment plans. In this study, we investigated 138 patients with HCC and presented a novel end-to-end deep learning strategy based on computed tomography (CT) radiomics (MVI-Mind), which integrates data preprocessing, automatic segmentation of lesions and other regions, automatic feature extraction, and MVI prediction. A lightweight transformer and a convolutional neural network (CNN) were proposed for the segmentation and prediction modules, respectively. To demonstrate the superiority of MVI-Mind, we compared the framework’s performance with that of current, mainstream segmentation, and classification models. The test results showed that MVI-Mind returned the best performance in both segmentation and prediction. The mean intersection over union (mIoU) of the segmentation module was 0.9006, and the area under the receiver operating characteristic curve (AUC) of the prediction module reached 0.9223. Additionally, it only took approximately 1 min to output a prediction for each patient, end-to-end using our computing device, which indicated that MVI-Mind could noninvasively, efficiently, and accurately predict the presence of MVI in HCC patients before surgery. This result will be helpful for doctors to make rational clinical decisions.

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Section: Methodsmentioning

confidence: 99%

MVI-Mind: A Novel Deep-Learning Strategy Using Computed Tomography (CT)-Based Radiomics for End-to-End High Efficiency Prediction of Microvascular Invasion in Hepatocellular Carcinoma

Wang

et al. 2022

Cancers

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“…In the computer vision community, many approaches and frameworks have been proposed to help in recognizing and monitoring COVID-19 infection [5]. Since the deep learning approach has proved its efficiency in most computer vision tasks [11] (including medical imaging tasks [3,12]), most of the approaches that have been proposed for COVID-19 analysis from CT scans are CNN-based approaches. In general, the approaches that use CT scans for COVID-19 analysis can be classified into: segmentation [13][14][15] and recognition [16][17][18][19][20] approaches.…”

Section: Related Workmentioning

confidence: 99%

Recognition of COVID-19 from CT Scans Using Two-Stage Deep-Learning-Based Approach: CNR-IEMN

Bougourzi

Contino

Distante

et al. 2021

Sensors

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Since the appearance of the COVID-19 pandemic (at the end of 2019, Wuhan, China), the recognition of COVID-19 with medical imaging has become an active research topic for the machine learning and computer vision community. This paper is based on the results obtained from the 2021 COVID-19 SPGC challenge, which aims to classify volumetric CT scans into normal, COVID-19, or community-acquired pneumonia (Cap) classes. To this end, we proposed a deep-learning-based approach (CNR-IEMN) that consists of two main stages. In the first stage, we trained four deep learning architectures with a multi-tasks strategy for slice-level classification. In the second stage, we used the previously trained models with an XG-boost classifier to classify the whole CT scan into normal, COVID-19, or Cap classes. Our approach achieved a good result on the validation set, with an overall accuracy of 87.75% and 96.36%, 52.63%, and 95.83% sensitivities for COVID-19, Cap, and normal, respectively. On the other hand, our approach achieved fifth place on the three test datasets of SPGC in the COVID-19 challenge, where our approach achieved the best result for COVID-19 sensitivity. In addition, our approach achieved second place on two of the three testing sets.

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“…Colorectal cancer arises in the colon (large intestine). This cancer is usually seen in aged people and now due to the changes in the lifestyle, it can also be seen in younger people [1][2][3]. For screening and diagnosing cancer, colonoscopy is the procedure for endoscopy.…”

Section: Introductionmentioning

confidence: 99%

Colorectal Cancer Disease Classification and Segmentation Using A Novel Deep Learning Approach

2022

IJIES

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In the world, with a death rate of about 35% of colorectal cancer aids as the most widespread type of tumor. The third most commonly diagnosed cancer compared to breast and lung cancer is colorectal cancer. Specifically, in the minimization of health inequalities, it can be supported by the clinical care of AI guidance. Therefore in this paper an effective classification is performed by the deep learning technique called MobileNetV2. Before the classification task, cycle GAN based data augmentation technique is applied to solve the data imbalance problem. Moreover, Mask R-CNN based segmentation is applied to improve the classifier's performance. For the classification of colorectal cancer, the proposed method is examined on a colorectal histopathological dataset (MNIST). The performance of the work will be estimated using precision, accuracy, F-score and recall metrics and the simulation results shows that the proposed classifier achieves 99.91% accuracy, 100% precision, 99.87% recall and 0.999% f1-score over the other state-of-art techniques.

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Two Ensemble-CNN Approaches for Colorectal Cancer Tissue Type Classification

Cited by 38 publications

References 37 publications

MVI-Mind: A Novel Deep-Learning Strategy Using Computed Tomography (CT)-Based Radiomics for End-to-End High Efficiency Prediction of Microvascular Invasion in Hepatocellular Carcinoma

MVI-Mind: A Novel Deep-Learning Strategy Using Computed Tomography (CT)-Based Radiomics for End-to-End High Efficiency Prediction of Microvascular Invasion in Hepatocellular Carcinoma

Recognition of COVID-19 from CT Scans Using Two-Stage Deep-Learning-Based Approach: CNR-IEMN

Colorectal Cancer Disease Classification and Segmentation Using A Novel Deep Learning Approach

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