Automated Detection and Characterization of Colon Cancer with Deep Convolutional Neural Networks

Hasan, Md. Imran; Ali, Md Shahin; Rahman, Habibur; Islam, Khairul

doi:10.1155/2022/5269913

Cited by 32 publications

(17 citation statements)

References 57 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A comparison of the proposed model with other models is shown in Table 5 . Based on binary classification using the LC25000 dataset, in [ 36 ], the authors used a CNN with PACC = 99.80, REC = 99.87, and F1 = 99.87. In [ 38 ], the authors used XGBoost with ACC = 99.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Automated Diagnosis for Colon Cancer Diseases Using Stacking Transformer Models and Explainable Artificial Intelligence

Gabralla,

Hussien,

AlMohimeed

et al. 2023

Diagnostics

View full text Add to dashboard Cite

Colon cancer is the third most common cancer type worldwide in 2020, almost two million cases were diagnosed. As a result, providing new, highly accurate techniques in detecting colon cancer leads to early and successful treatment of this disease. This paper aims to propose a heterogenic stacking deep learning model to predict colon cancer. Stacking deep learning is integrated with pretrained convolutional neural network (CNN) models with a metalearner to enhance colon cancer prediction performance. The proposed model is compared with VGG16, InceptionV3, Resnet50, and DenseNet121 using different evaluation metrics. Furthermore, the proposed models are evaluated using the LC25000 and WCE binary and muticlassified colon cancer image datasets. The results show that the stacking models recorded the highest performance for the two datasets. For the LC25000 dataset, the stacked model recorded the highest performance accuracy, recall, precision, and F1 score (100). For the WCE colon image dataset, the stacked model recorded the highest performance accuracy, recall, precision, and F1 score (98). Stacking-SVM achieved the highest performed compared to existing models (VGG16, InceptionV3, Resnet50, and DenseNet121) because it combines the output of multiple single models and trains and evaluates a metalearner using the output to produce better predictive results than any single model. Black-box deep learning models are represented using explainable AI (XAI).

show abstract

Section: Resultsmentioning

confidence: 99%

“…From the results, the proposed method achieved a high accuracy. In [ 36 ], the authors suggested a DCNN model for classification of benign and adenocarcinoma colon tissues. They used the LC25000 dataset.…”

Section: Related Workmentioning

confidence: 99%

Automated Diagnosis for Colon Cancer Diseases Using Stacking Transformer Models and Explainable Artificial Intelligence

Gabralla,

Hussien,

AlMohimeed

et al. 2023

Diagnostics

View full text Add to dashboard Cite

show abstract

“…In another recent study using LC25000 dataset [22], lung cancer subtyping is performed using a custom-made CNN, obtaining an accuracy of 97.2%. Furthermore, in [23], colon cancer subtyping was only implemented using a CNN and principal component analysis (PCA) from LC25000, and the framework has a classification accuracy of 99.8%. Few studies exist using feature extraction from the histopathology images and different ML classifiers, including random forest (RF) and XGBoost, for the lung and colon cancer subtyping and achieved accuracies above 96.3% [24,25].…”

Section: Related Workmentioning

confidence: 99%

An Explainable Classification Method Based on Complex Scaling in Histopathology Images for Lung and Colon Cancer

et al. 2023

View full text Add to dashboard Cite

Lung and colon cancers are among the leading causes of human mortality and morbidity. Early diagnostic work up of these diseases include radiography, ultrasound, magnetic resonance imaging, and computed tomography. Certain blood tumor markers for carcinoma lung and colon also aid in the diagnosis. Despite the lab and diagnostic imaging, histopathology remains the gold standard, which provides cell-level images of tissue under examination. To read these images, a histopathologist spends a large amount of time. Furthermore, using conventional diagnostic methods involve high-end equipment as well. This leads to limited number of patients getting final diagnosis and early treatment. In addition, there are chances of inter-observer errors. In recent years, deep learning has shown promising results in the medical field. This has helped in early diagnosis and treatment according to severity of disease. With the help of EfficientNetV2 models that have been cross-validated and tested fivefold, we propose an automated method for detecting lung (lung adenocarcinoma, lung benign, and lung squamous cell carcinoma) and colon (colon adenocarcinoma and colon benign) cancer subtypes from LC25000 histopathology images. A state-of-the-art deep learning architecture based on the principles of compound scaling and progressive learning, EfficientNetV2 large, medium, and small models. An accuracy of 99.97%, AUC of 99.99%, F1-score of 99.97%, balanced accuracy of 99.97%, and Matthew’s correlation coefficient of 99.96% were obtained on the test set using the EffcientNetV2-L model for the 5-class classification of lung and colon cancers, outperforming the existing methods. Using gradCAM, we created visual saliency maps to precisely locate the vital regions in the histopathology images from the test set where the models put more attention during cancer subtype predictions. This visual saliency maps may potentially assist pathologists to design better treatment strategies. Therefore, it is possible to use the proposed pipeline in clinical settings for fully automated lung and colon cancer detection from histopathology images with explainability.

show abstract

“…Their proposed model achieved 99.58% accuracy for lung and colon abnormalities based on histopathological images. On the other hand, Hasan et al [ 57 ] used the deep CNN model for detecting and classifying colon cancer using colon images of the LC25000 dataset. In this colon data, there were 10,000 histopathological images of colon samples.…”

Section: Literature Surveymentioning

confidence: 99%

A Framework for Lung and Colon Cancer Diagnosis via Lightweight Deep Learning Models and Transformation Methods

2022

View full text Add to dashboard Cite

Among the leading causes of mortality and morbidity in people are lung and colon cancers. They may develop concurrently in organs and negatively impact human life. If cancer is not diagnosed in its early stages, there is a great likelihood that it will spread to the two organs. The histopathological detection of such malignancies is one of the most crucial components of effective treatment. Although the process is lengthy and complex, deep learning (DL) techniques have made it feasible to complete it more quickly and accurately, enabling researchers to study a lot more patients in a short time period and for a lot less cost. Earlier studies relied on DL models that require great computational ability and resources. Most of them depended on individual DL models to extract features of high dimension or to perform diagnoses. However, in this study, a framework based on multiple lightweight DL models is proposed for the early detection of lung and colon cancers. The framework utilizes several transformation methods that perform feature reduction and provide a better representation of the data. In this context, histopathology scans are fed into the ShuffleNet, MobileNet, and SqueezeNet models. The number of deep features acquired from these models is subsequently reduced using principal component analysis (PCA) and fast Walsh–Hadamard transform (FHWT) techniques. Following that, discrete wavelet transform (DWT) is used to fuse the FWHT’s reduced features obtained from the three DL models. Additionally, the three DL models’ PCA features are concatenated. Finally, the diminished features as a result of PCA and FHWT-DWT reduction and fusion processes are fed to four distinct machine learning algorithms, reaching the highest accuracy of 99.6%. The results obtained using the proposed framework based on lightweight DL models show that it can distinguish lung and colon cancer variants with a lower number of features and less computational complexity compared to existing methods. They also prove that utilizing transformation methods to reduce features can offer a superior interpretation of the data, thus improving the diagnosis procedure.

show abstract

Automated Detection and Characterization of Colon Cancer with Deep Convolutional Neural Networks

Cited by 32 publications

References 57 publications

Automated Diagnosis for Colon Cancer Diseases Using Stacking Transformer Models and Explainable Artificial Intelligence

Automated Diagnosis for Colon Cancer Diseases Using Stacking Transformer Models and Explainable Artificial Intelligence

An Explainable Classification Method Based on Complex Scaling in Histopathology Images for Lung and Colon Cancer

A Framework for Lung and Colon Cancer Diagnosis via Lightweight Deep Learning Models and Transformation Methods

Contact Info

Product

Resources

About