Deep Neural Network Models for Colon Cancer Screening

Kavitha, Muthu Subash; Gangadaran, Prakash; Jackson, Aurelia; Maran, Balu Alagar Venmathi; Kurita, Takio; Ahn, Byeong‐Cheol

doi:10.3390/cancers14153707

Cited by 18 publications

(7 citation statements)

References 80 publications

(115 reference statements)

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“…For example, a hybrid XAI model might combine a rulebased system with a deep learning model. The rule-based system would provide an initial set of predictions based on expert knowledge or experience, and the deep learning model would then refine these predictions based on the relationships it has learned from the data [73]. The hybrid model could then provide an explanation of its predictions by combining the explanations provided by the rule-based system and the deep learning model.…”

Section: ) Hybrid Modelsmentioning

confidence: 99%

Explainable Artificial Intelligence for Drug Discovery and Development: A Comprehensive Survey

Alizadehsani,

Oyelere,

Hussain

et al. 2024

IEEE Access

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The field of drug discovery has experienced a remarkable transformation with the advent of artificial intelligence (AI) and machine learning (ML) technologies. However, as these AI and ML models are becoming more complex, there is a growing need for transparency and interpretability of the models. Explainable Artificial Intelligence (XAI) is a novel approach that addresses this issue and provides a more interpretable understanding of the predictions made by machine learning models. In recent years, there has been an increasing interest in the application of XAI techniques to drug discovery. This review article provides a comprehensive overview of the current state-of-the-art in XAI for drug discovery, including various XAI methods, their application in drug discovery, and the challenges and limitations of XAI techniques in drug discovery. The article also covers the application of XAI in drug discovery, including target identification, compound design, and toxicity prediction. Furthermore, the article suggests potential future research directions for the application of XAI in drug discovery. This review article aims to provide a comprehensive understanding of the current state of XAI in drug discovery and its potential to transform the field.

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Section: ) Hybrid Modelsmentioning

confidence: 99%

Explainable Artificial Intelligence for Drug Discovery and Development: A Comprehensive Survey

Alizadehsani,

Oyelere,

Hussain

et al. 2024

IEEE Access

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“…Although machine-learning techniques have been successfully implemented in various domains, including healthcare, ANN has only been sporadically used as a predictive instrument for colorectal cancer surgery [ 10 , 11 , 12 ]. Most often, ANNs proved their efficiency in colorectal cancer screening, diagnosis, survival assessment, and even as prediction tools for lymphovascular invasion in CRC [ 13 , 14 , 15 , 16 ].…”

Section: Introductionmentioning

confidence: 99%

Preoperative Immunocite-Derived Ratios Predict Surgical Complications Better when Artificial Neural Networks Are Used for Analysis—A Pilot Comparative Study

Pătrașcu

Cotofana-Graure

Șurlin

et al. 2023

JPM

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We aimed to comparatively assess the prognostic preoperative value of the main peripheral blood components and their ratios—the systemic immune-inflammation index (SII), neutrophil-to-lymphocyte ratio (NLR), lymphocyte-to-monocyte ratio (LMR), and platelet-to-lymphocyte ratio (PLR)—to the use of artificial-neural-network analysis in determining undesired postoperative outcomes in colorectal cancer patients. Our retrospective study included 281 patients undergoing elective radical surgery for colorectal cancer in the last seven years. The preoperative values of SII, NLR, LMR, and PLR were analyzed in relation to postoperative complications, with a special emphasis on their ability to accurately predict the occurrence of anastomotic leak. A feed-forward fully connected multilayer perceptron network (MLP) was trained and tested alongside conventional statistical tools to assess the predictive value of the abovementioned blood markers in terms of sensitivity and specificity. Statistically significant differences and moderate correlation levels were observed for SII and NLR in predicting the anastomotic leak rate and degree of postoperative complications. No correlations were found between the LMR and PLR or the abovementioned outcomes. The MLP network analysis showed superior prediction value in terms of both sensitivity (0.78 ± 0.07; 0.74 ± 0.04; 0.71 ± 0.13) and specificity (0.81 ± 0.11; 0.69 ± 0.03; 0.9 ± 0.04) for all the given tasks. Preoperative SII and NLR appear to be modest prognostic factors for anastomotic leakage and overall morbidity. Using an artificial neural network offers superior prognostic results in the preoperative risk assessment for overall morbidity and anastomotic leak rate.

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“…Research by M S Kavitha et al [10]. discusses the use of many different deep learning models for detecting colon cancer.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Colorectal Carcinoma Prediction Employing Convolutional Neural Networks with Multiple Features and Fusion Layers

Raju

Jayavel

Rajalakshmi

2023

Preprint

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The catastrophic impacts of the lethal illness known as intestinal cancer are felt by those who suffer from it. Colonoscopy is the most effective cancer screening procedure currently available. While several deep learning methods are already used by existing systems, little research has been conducted on feature extraction and testing in the context of cancer diagnosis. The suggested system is a more comprehensive implementation of CADx technology that relies on intelligent detection. Data from the CKHK-22 dataset was preprocessed and kept in the cloud before being utilized for the system's training and testing purposes. To create local binary pattern (LBP) features, we first grayscale the colour features included in the colonoscopy dataset images. Several convolutional neural networks (CNNs), including VGG-16, DenseNet-201, and ResNet-50, are trained on the CKHK-22 dataset after it has been transformed to three features. In a vital step, the CKHK-22's feature acquisitions are fused to produce new features that are then employed in the DV-22, RD-22, and RDV-22 fusion CNN models, respectively. The CADx system, transfer learning with LSTM, and DenseNet-201 all attained testing accuracy rates of 91.92%, 86.76%, and 68.56%, respectively, over the original, grayscale, and LBP image feature datasets. By initially collecting the combined features of the original, grey, and LBP features through transfer learning with LSTM, the RDV-22 model obtained the best testing accuracy (90.81%) while applying fusion CNN for feature acquisition. The CKHK-22 dataset was mined for its three most distinguishing features by this CADx system, which yielded sufficient accuracy with DenseNet-201 and merged features and optimal accuracy with RDV-22 fusion CNN in diagnosing cancer. Using feature extraction, cancer diagnoses may be predicted with high precision.

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Deep Neural Network Models for Colon Cancer Screening

Cited by 18 publications

References 80 publications

Explainable Artificial Intelligence for Drug Discovery and Development: A Comprehensive Survey

Explainable Artificial Intelligence for Drug Discovery and Development: A Comprehensive Survey

Preoperative Immunocite-Derived Ratios Predict Surgical Complications Better when Artificial Neural Networks Are Used for Analysis—A Pilot Comparative Study

Optimizing Colorectal Carcinoma Prediction Employing Convolutional Neural Networks with Multiple Features and Fusion Layers

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