Colon Cancer Classification Research Articles

Data-driven color augmentation for H&E stained images in computational pathology

Computational pathology targets the automatic analysis of Whole Slide Images (WSI). WSIs are high-resolution digitized histopathology images, stained with chemical reagents to highlight specific tissue structures and scanned via whole slide scanners. The application of different parameters during WSI acquisition may lead to stain color heterogeneity, especially considering samples collected from several medical centers. Dealing with stain color heterogeneity often limits the robustness of methods developed to analyze WSIs, in particular Convolutional Neural Networks (CNN), the state-of-the-art algorithm for most computational pathology tasks. Stain color heterogeneity is still an unsolved problem, although several methods have been developed to alleviate it, such as Hue-Saturation-Contrast (HSC) color augmentation and stain augmentation methods. The goal of this paper is to present Data-Driven Color Augmentation (DDCA), a method to improve the efficiency of color augmentation methods by increasing the reliability of the samples used for training computational pathology models. During CNN training, a database including over 2 million H&E color variations collected from private and public datasets is used as a reference to discard augmented data with color distributions that do not correspond to realistic data. DDCA is applied to HSC color augmentation, stain augmentation and H&E-adversarial networks in colon and prostate cancer classification tasks. DDCA is then compared with 11 state-of-the-art baseline methods to handle color heterogeneity, showing that it can substantially improve classification performance on unseen data including heterogeneous color variations.

Reinforcing Artificial Neural Networks through Traditional Machine Learning Algorithms for Robust Classification of Cancer

Machine Learning (ML)-based prediction and classification systems employ data and learning algorithms to forecast target values. However, improving predictive accuracy is a crucial step for informed decision-making. In the healthcare domain, data are available in the form of genetic profiles and clinical characteristics to build prediction models for complex tasks like cancer detection or diagnosis. Among ML algorithms, Artificial Neural Networks (ANNs) are considered the most suitable framework for many classification tasks. The network weights and the activation functions are the two crucial elements in the learning process of an ANN. These weights affect the prediction ability and the convergence efficiency of the network. In traditional settings, ANNs assign random weights to the inputs. This research aims to develop a learning system for reliable cancer prediction by initializing more realistic weights computed using a supervised setting instead of random weights. The proposed learning system uses hybrid and traditional machine learning techniques such as Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), Random Forest (RF), k-Nearest Neighbour (kNN), and ANN to achieve better accuracy in colon and breast cancer classification. This system computes the confusion matrix-based metrics for traditional and proposed frameworks. The proposed framework attains the highest accuracy of 89.24 percent using the colon cancer dataset and 72.20 percent using the breast cancer dataset, which outperforms the other models. The results show that the proposed learning system has higher predictive accuracies than conventional classifiers for each dataset, overcoming previous research limitations. Moreover, the proposed framework is of use to predict and classify cancer patients accurately. Consequently, this will facilitate the effective management of cancer patients.

Lung and colon cancer classification using hybrid principle component analysis network‐extreme learning machine

SummaryLung and colon cancers are dangerous diseases that can grow in organs and create a negative impact on human life in certain cases. The histological detection of such malignancies is one of the most critical parts of optimal treatment. As a result, the important objective of this article is to create an effective computerized diagnosis system for identifying adenocarcinomas of the colon as well as, adenocarcinomas and squamous cell carcinomas of the lungs using digital histopathology images and the combination of deep and machine learning techniques. For this, an effective optimized hybrid deep and machine learning framework is developed. This framework consists of two stages. In the first stage, the features of lung and colon images are extracted by principle component analysis network. Then the effective classification is conducted based on extreme learning machine (ELM) with the rider optimization algorithm which classifies lung and colon cancer into five types. The empirical investigation shows that the classification results on the benchmark LC25000 dataset have improved significantly. The use of this model will aid medical professionals in the development of an automatic and reliable system for detecting various forms of lung and colon cancers.

Lung and colon cancer classification using medical imaging: a feature engineering approach.

Lung and colon cancers lead to a significant portion of deaths. Their simultaneous occurrence is uncommon, however, in the absence of early diagnosis, the metastasis of cancer cells is very high between these two organs. Currently, histopathological diagnosis and appropriate treatment are the only way to improve the chances of survival and reduce cancer mortality. Using artificial intelligence in the histopathological diagnosis of colon and lung cancer can provide significant help to specialists in identifying cases of colon and lung cancers with less effort, time and cost. The objective of this study is to set up a computer-aided diagnostic system that can accurately classify five types of colon and lung tissues (two classes for colon cancer and three classes for lung cancer) by analyzing their histopathological images. Using machine learning, features engineering and image processing techniques, the six models XGBoost, SVM, RF, LDA, MLP and LightGBM were used to perform the classification of histopathological images of lung and colon cancers that were acquired from the LC25000 dataset. The main advantage of using machine learning models is that they allow a better interpretability of the classification model since they are based on feature engineering; however, deep learning models are black box networks whose working is very difficult to understand due to the complex network design. The acquired experimental results show that machine learning models give satisfactory results and are very precise in identifying classes of lung and colon cancer subtypes. The XGBoost model gave the best performance with an accuracy of 99% and a F1-score of 98.8%. The implementation and the development of this model will help healthcare specialists identify types of colon and lung cancers. The code will be available upon request.

Microscopic nuclei classification, segmentation, and detection with improved deep convolutional neural networks (DCNN)

BackgroundNuclei classification, segmentation, and detection from pathological images are challenging tasks due to cellular heterogeneity in the Whole Slide Images (WSI).MethodsIn this work, we propose advanced DCNN models for nuclei classification, segmentation, and detection tasks. The Densely Connected Neural Network (DCNN) and Densely Connected Recurrent Convolutional Network (DCRN) models are applied for the nuclei classification tasks. The Recurrent Residual U-Net (R2U-Net) and the R2UNet-based regression model named the University of Dayton Net (UD-Net) are applied for nuclei segmentation and detection tasks respectively. The experiments are conducted on publicly available datasets, including Routine Colon Cancer (RCC) classification and detection and the Nuclei Segmentation Challenge 2018 datasets for segmentation tasks. The experimental results were evaluated with a five-fold cross-validation method, and the average testing results are compared against the existing approaches in terms of precision, recall, Dice Coefficient (DC), Mean Squared Error (MSE), F1-score, and overall testing accuracy by calculating pixels and cell-level analysis.ResultsThe results demonstrate around 2.6% and 1.7% higher performance in terms of F1-score for nuclei classification and detection tasks when compared to the recently published DCNN based method. Also, for nuclei segmentation, the R2U-Net shows around 91.90% average testing accuracy in terms of DC, which is around 1.54% higher than the U-Net model.ConclusionThe proposed methods demonstrate robustness with better quantitative and qualitative results in three different tasks for analyzing the WSI.

Comparison of Convolution Neural Network Architecture for Colon Cancer Classification

In 2021, colon cancer is the second most common cause of death for this type of cancer. Therefore, in this study, a colon cancer classification system was developed to help medical staff classify 2 types of cancer colon adenocarcinomas and benign colonic tissues. The classification method uses the Convolution Neural Network (CNN) with the architecture VGG16, VGG19, ResNet101, ResNet152, MobileNetV2, DenseNet201 and InceptionV3. We used 10.000 image datasets that divided into 7200 training data, 1800 validation data and 1000 test data. Pre-trained models are used to extract new features and training data. The best performance parameter based on accuracy, precision, recall and f1-score and confusion matrix are obtained in 3 architectures, namely VGG19, ResNet101 and ResNet152. These architectures can identify and classify both types of colon cancer with 100% accuracy.

An empirical study of handcrafted and dense feature extraction techniques for lung and colon cancer classification from histopathological images

According to a 2020 WHO report, cancer is one of the main causes of deaths worldwide. Among these deaths, lung and colon cancer collectively responsible for nearly 2.735 million deaths.So, detection and classification of lung and colon cancer is one of the utmost priority research areas in the field of biomedical health informatics. In this article, comparative analysis of two feature extraction methodologies has been presented for lung and colon cancer classification. In one approach, six handcrafted features extraction techniques based on colour, texture, shape and structure are presented. Gradient Boosting (GB), SVM-RBF, Multilayer Perceptron (MLP) and Random Forest (RF) classifiers with handcrafted features are trained and tested for lung and colon cancer classification. In another approach, using the notion of transfer learning, seven deep learning frameworks for deep feature extraction from lung and colon cancer histopathological images are presented. The extracted deep features (as input attributes) are applied into conventional GB, SVM-RBF, MLP and RF classifiers for lung and colon cancer classification. However, in contrast to handcrafted features a significant improvement in classifiers performance is observed with features extracted by deep CNN networks. It has been found that the proposed technique obtained excellent results in terms of accuracy, precision, recall, F1 score and ROC-AUC. The RF classifier with DenseNet-121 extracted deep features can identify the lung and colon cancer tissue with an accuracy and recall of 98.60%, precision of 98.63%, F1 score of 0.985 and ROC-AUC of 01.

Multi-Input Dual-Stream Capsule Network for Improved Lung and Colon Cancer Classification.

Lung and colon cancers are two of the most common causes of death and morbidity in humans. One of the most important aspects of appropriate treatment is the histopathological diagnosis of such cancers. As a result, the main goal of this study is to use a multi-input capsule network and digital histopathology images to build an enhanced computerized diagnosis system for detecting squamous cell carcinomas and adenocarcinomas of the lungs, as well as adenocarcinomas of the colon. Two convolutional layer blocks are used in the proposed multi-input capsule network. The CLB (Convolutional Layers Block) employs traditional convolutional layers, whereas the SCLB (Separable Convolutional Layers Block) employs separable convolutional layers. The CLB block takes unprocessed histopathology images as input, whereas the SCLB block takes uniquely pre-processed histopathological images. The pre-processing method uses color balancing, gamma correction, image sharpening, and multi-scale fusion as the major processes because histopathology slide images are typically red blue. All three channels (Red, Green, and Blue) are adequately compensated during the color balancing phase. The dual-input technique aids the model’s ability to learn features more effectively. On the benchmark LC25000 dataset, the empirical analysis indicates a significant improvement in classification results. The proposed model provides cutting-edge performance in all classes, with 99.58% overall accuracy for lung and colon abnormalities based on histopathological images.

Enhancing the performance of hybrid evolutionary algorithms in microarray colon data classification

AbstractMicroarray technology aids in monitoring simultaneous gene expressions of human cells for diagnosis and treatment of infectious diseases in a single experiment. The microarray data are of high dimension with a large number of genes and fewer samples. Highly informative features and appropriate classifiers are necessary to increase the accuracy of disease classification. In this paper, a publicly available colon cancer dataset is analyzed for classification. At first, Power Spectral Density (PSD) technique is employed to obtain the reduced gene features and after that, they are classified using various types of classifiers for colon cancer classification. To improve the classification accuracy, the hybridized evolutionary classifiers, namely, Artificial Bee Colony‐Firefly (ABC‐Firefly), Artificial Bee Colony‐Particle Swarm Optimization (ABC‐PSO), Particle Swarm Optimization‐Firefly (PSO‐Firefly) are derived from the conventional evolutionary classifiers, namely, Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO) and Firefly classifiers. The performance of the three hybridized evolutionary algorithms is compared with the conventional classifiers. Additionally, the comparison was also performed on the conventional and hybridized evolutionary algorithms using Expectation Maximization (EM) and cascaded Power Spectral Density‐Expectation Maximization (PSD‐EM) dimensionality reduction techniques. Although there are many classifiers for colon classification, it is wise to use PSD features with a Hybrid ABC‐PSO classifier for enhanced performance. The experimental results reveal that the Hybrid ABC‐PSO classifier with PSD features achieves 98.96% for colon cancer samples and 100% accuracy for colon normal samples when compared with the other classifiers reported in the literature.

A framework model using multifilter feature selection to enhance colon cancer classification.

Gene expression profiles can be utilized in the diagnosis of critical diseases such as cancer. The selection of biomarker genes from these profiles is significant and crucial for cancer detection. This paper presents a framework proposing a two-stage multifilter hybrid model of feature selection for colon cancer classification. Colon cancer is being extremely common nowadays among other types of cancer. There is a need to find fast and an accurate method to detect the tissues, and enhance the diagnostic process and the drug discovery. This paper reports on a study whose objective has been to improve the diagnosis of cancer of the colon through a two-stage, multifilter model of feature selection. The model described deals with feature selection using a combination of Information Gain and a Genetic Algorithm. The next stage is to filter and rank the genes identified through this method using the minimum Redundancy Maximum Relevance (mRMR) technique. The final phase is to further analyze the data using correlated machine learning algorithms. This two-stage approach, which involves the selection of genes before classification techniques are used, improves success rates for the identification of cancer cells. It is found that Decision Tree, K-Nearest Neighbor, and Naïve Bayes classifiers had showed promising accurate results using the developed hybrid framework model. It is concluded that the performance of our proposed method has achieved a higher accuracy in comparison with the existing methods reported in the literatures. This study can be used as a clue to enhance treatment and drug discovery for the colon cancer cure.

DHS‐CapsNet: Dual horizontal squash capsule networks for lung and colon cancer classification from whole slide histopathological images

AbstractThis paper proposes a new dual horizontal squash capsule network (DHS‐CapsNet) to classify the lung and colon cancers on histopathological images. DHS‐CapsNet is made up of encoder feature fusion (EFF) and a novel horizontal squash (HSquash) function. The EFF aggregates the extracted feature from the 2‐lane convolutional layers, which provides rich information for better accuracy. HSquash is proposed as a squash function to ensure that vectors are effectively squashed and produces sparsity for a high discriminative capsule to extract important information from images with varied backgrounds. To present the effectiveness of DHS‐CapsNet empirically, we applied this method on histopathological images (LC25000 dataset). We achieved better results of 99.23% compared to traditional CapsNet (85.55%). The DHS‐CapsNet provides the top‐1 classification error of 0.77% compared to 14.45% of the traditional CapsNet. Our results illustrate that our method improves CapsNet and can be adopted as a computer‐aided diagnostic method to support doctors in lung and colon cancer diagnostics.

Hyperspectral imaging for colon cancer classification in surgical specimens: towards optical biopsy during image-guided surgery.

The main curative treatment for localized colon cancer is surgical resection. However when tumor residuals are left positive margins are found during the histological examinations and additional treatment is needed to inhibit recurrence. Hyperspectral imaging (HSI) can offer non-invasive surgical guidance with the potential of optimizing the surgical effectiveness. In this paper we investigate the capability of HSI for automated colon cancer detection in six ex-vivo specimens employing a spectral-spatial patch-based classification approach. The results demonstrate the feasibility in assessing the benign and malignant boundaries of the lesion with a sensitivity of 0.88 and specificity of 0.78. The results are compared with the state-of-the-art deep learning based approaches. The method with a new hybrid CNN outperforms the state-of the-art approaches (0.74 vs. 0.82 AUC). This study paves the way for further investigation towards improving surgical outcomes with HSI.

Breast and Colon Cancer Classification from Gene Expression Profiles Using Data Mining Techniques

Early detection of cancer increases the probability of recovery. This paper presents an intelligent decision support system (IDSS) for the early diagnosis of cancer based on gene expression profiles collected using DNA microarrays. Such datasets pose a challenge because of the small number of samples (no more than a few hundred) relative to the large number of genes (in the order of thousands). Therefore, a method of reducing the number of features (genes) that are not relevant to the disease of interest is necessary to avoid overfitting. The proposed methodology uses the information gain (IG) to select the most important features from the input patterns. Then, the selected features (genes) are reduced by applying the grey wolf optimization (GWO) algorithm. Finally, the methodology employs a support vector machine (SVM) classifier for cancer type classification. The proposed methodology was applied to two datasets (Breast and Colon) and was evaluated based on its classification accuracy, which is the most important performance measure in disease diagnosis. The experimental results indicate that the proposed methodology is able to enhance the stability of the classification accuracy as well as the feature selection.

A novel high-risk stage II colon cancer classification method may improve prognosis with patients who receive adjuvant chemotherapy

A novel high-risk stage II colon cancer classification method may improve prognosis with patients who receive adjuvant chemotherapy

A molecular sub-cluster of colon cancer cells with low VDR expression is sensitive to chemotherapy, BRAF inhibitors and PI3K-mTOR inhibitors treatment.

Gene expression based consensus molecular subtypes (CMS) and non-negative matrix factorization (NMF) sub-clusters are robust colon cancer classification systems. Although, the molecular features are clear, colon cancer subgroups based interventions are limited. To address this problem, we analyze the CMS and NMF subgroup guided drug sensitivity in colon cancer cell lines. CMS3 subtype cells are sensitive to 5-Fluorouracil, while, CMS4 subtype cells are sensitive to cisplatin treatment. In NMF classification, a sub-cluster is specifically sensitive to chemotherapy, BRAF inhibitors, PI3K-mTOR inhibitors and NOTCH inhibitor treatment. This sub-cluster has low frequency of TP53, POLE, PIK3CA and BRAF mutation. Transcriptional analysis demonstrates low NOTCH signaling activity, low CDX2 and VDR expression in this sub-cluster. CDX2 and VDR are significantly associated with the sensitivity of chemotherapy, BRAF inhibitors and PI3K-mTOR inhibitors. Moreover, a positive correlation between VDR and CDX2 is identified. VDR and CDX2 mediated regulatory networks are constructed. At last, three or four sub-clusters classification is validated in colon cancer patients. Overall, our results suggest a molecular sub-cluster of colon cancer cells with low CDX2 and VDR expression is sensitive to chemotherapy, BRAF inhibitors and PI3K-mTOR inhibitors treatment and provide an example of translation of cancer classification to subgroup guided therapies.

Deep transfer learning methods for colon cancer classification in confocal laser microscopy images.

The gold standard for colorectal cancer metastases detection in the peritoneum is histological evaluation of a removed tissue sample. For feedback during interventions, real-time in vivo imaging with confocal laser microscopy has been proposed for differentiation of benign and malignant tissue by manual expert evaluation. Automatic image classification could improve the surgical workflow further by providing immediate feedback. We analyze the feasibility of classifying tissue from confocal laser microscopy in the colon and peritoneum. For this purpose, we adopt both classical and state-of-the-art convolutional neural networks to directly learn from the images. As the available dataset is small, we investigate several transfer learning strategies including partial freezing variants and full fine-tuning. We address the distinction of different tissue types, as well as benign and malignant tissue. We present a thorough analysis of transfer learning strategies for colorectal cancer with confocal laser microscopy. In the peritoneum, metastases are classified with an AUC of 97.1, and in the colon the primarius is classified with an AUC of 73.1. In general, transfer learning substantially improves performance over training from scratch. We find that the optimal transfer learning strategy differs for models and classification tasks. We demonstrate that convolutional neural networks and transfer learning can be used to identify cancer tissue with confocal laser microscopy. We show that there is no generally optimal transfer learning strategy and model as well as task-specific engineering is required. Given the high performance for the peritoneum, even with a small dataset, application for intraoperative decision support could be feasible.

Analysis of the influence of Minimum Redundancy Maximum Relevance as dimensionality reduction method on cancer classification based on microarray data using Support Vector Machine classifier

Microarray technology provides a way to monitor thousands of gene expressions at the same time. However, microarray data has a high dimensionality. This high dimensionality will affect the classification performance. In order to solve this issue, this research proposed the use of Minimum Redundancy Maximum Relevance (MRMR) as the dimension reduction method and Support Vector Machine (SVM) as the classifier. Principal Component Analysis (PCA) method was also used as a comparison to MRMR. Tests on lung cancer and ovarian cancer data with MRMR and SVM linear kernel classifier as well as polynomial kernel resulted in an F1-score of 1, with the number of features used for the classification was 20% of the original feature dataset. This means that the accuracy of the classification was 100% and the system built has an excellent performance. As for the colon cancer classification, the F1-score result using MRMR and SVM polynomial kernel classifier was greater than the classification without the dimension reduction method, which is 0.84. It is the same with the classification of leukemia cancer, where the MRMR and SVM polynomial kernel classifier obtained greater result than the result of leukemia classification without dimension reduction method, which the F1-score was 0.9657.

Genetic or non-genetic prognostic factors for colon cancer classification

Many researches have addressed patient classification using prognostic factors or gene expression profiles (GEPs). This study tried to identify whether a prognostic factor was genetic by using GEPs. If significant GEP difference was observed between two statuses of a factor, the factor might be genetic. If the GEP difference was largely less significant than the survival difference, the survival difference might not be due to the genes; then, the factor might be non-genetic or partly non-genetic. A practice was made in this study using public dataset GSE40967, which contains GEP data of 566 colon cancer patients, messages of tumour-node-metastasis (TNM) staging, etc. Prognostic factors T, N, M, and TNM were observed being non-genetic or partly non-genetic, which should be a complement for future gene expression classifiers.

Genetic or non-genetic prognostic factors for colon cancer classification

Genetic or non-genetic prognostic factors for colon cancer classification

Multi-label classification for colon cancer using histopathological images

Colon cancer classification has a significant guidance value in clinical diagnoses and medical prognoses. The classification of colon cancers with high accuracy is the premise of efficient treatment. Our task is to build a system for colon cancer detection and classification based on slide histopathological images. Some former researches focus on single label classification. Through analyzing large amount of colon cancer images, we found that one image may contain cancer regions of multiple types. Therefore, we reformulated the task as multi-label problem. Four kinds of features (Color Histogram, Gray-Level Co-occurrence Matrix, Histogram of Oriented Gradients and Euler number) were introduced to compose our discriminative feature set, extracted from our dataset that includes six single categories and four multi-label categories. In order to evaluate the performance and make comparison with our multi-label model, three commonly used multi-classification methods were designed in our experiment including one-against-all SVM (OAA), one-against-one SVM (OAO) and multi-structure SVM. Four indicators (Precision, Recall, F-measure, and Accuracy) under 3-fold cross-validation were used to validate the performance of our approach. Experiment results show that the precision, recall and F-measure of multi-label method as 73.7%, 68.2%, and 70.8% with all features, which are higher than the other three classifiers. These results demonstrate the effectiveness and efficiency of our method on colon histopathological images analysis.