Deep Learning Predictive Model for Colon Cancer Patient using CNN-based Classification

Tasnim, Zarrin; Chakraborty, Sovon; Shamrat, F. M. Javed Mehedi; Chowdhury, Ali Newaz; Nuha, Humaira Alam; Karim, Asif; Zahir, Sabrina Binte; Billah, Md. Masum

doi:10.14569/ijacsa.2021.0120880

Cited by 43 publications

(30 citation statements)

References 16 publications

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“…Because this is an unusual file type, the first step was to convert it to CSV. Results and discussions will explain why this conversion occurred [ 13 ].…”

Section: Methodsmentioning

confidence: 99%

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[Retracted] Hyperspectral Image Analysis of Colon Tissue and Deep Learning for Characterization of Health care

Abdulrazzaq

Hamid²,

Al-Douri

et al. 2022

Journal of Environmental and Public Health

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Colon cancer is a disease characterized by the unusual and uncontrolled development of cells that are found in the large intestine. If the tumour extends to the lower part of the colon (rectum), the cancer may be colorectal. Medical imaging is the denomination of methods used to create visual representations of the human body for clinical analysis, such as diagnosing, monitoring, and treating medical conditions. In this research, a computational proposal is presented to aid the diagnosis of colon cancer, which consists of using hyperspectral images obtained from slides with biopsy samples of colon tissue in paraffin, characterizing pixels so that, afterwards, imaging techniques can be applied. Using computer graphics augmenting conventional histological deep learning architecture, it can classify pixels in hyperspectral images as cancerous, inflammatory, or healthy. It is possible to find connections between histochemical characteristics and the absorbance of tissue under various conditions using infrared photons at various frequencies in hyperspectral imaging (HSI). Deep learning techniques were used to construct and implement a predictor to detect anomalies, as well as to develop a computer interface to assist pathologists in the diagnosis of colon cancer. An infrared absorbance spectrum of each of the pixels used in the developed classifier resulted in an accuracy level of 94% for these three classes.

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“…Because this is an unusual file type, the first step was to convert it to CSV. Results and discussions will explain why this conversion occurred [ 13 ].…”

Section: Methodsmentioning

confidence: 99%

“…Deep learning has recently enhanced HSI categorization [ 12 ]. Tasnim et al [ 13 ] created a five-layer CNN with basic CNN elements in the input layer. L'Heureux et al [ 14 ] used HSI to diagnose thyroid cancer, while Dariya et al [ 15 ] used HSI to diagnose colon cancer.…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Hyperspectral Image Analysis of Colon Tissue and Deep Learning for Characterization of Health care

Abdulrazzaq

Hamid²,

Al-Douri

et al. 2022

Journal of Environmental and Public Health

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“…A confusion matrix was used to show the performance results [ 62 ]. The matrix is composed of the following elements: True-positive (TP): refers to instances of COVID-19 that have been correctly classified.…”

Section: Proposed Methodsmentioning

confidence: 99%

COVID-19 Detection Using Deep Learning Algorithm on Chest X-ray Images

Akter

Shamrat

Chakraborty

et al. 2021

Biology

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COVID-19, regarded as the deadliest virus of the 21st century, has claimed the lives of millions of people around the globe in less than two years. Since the virus initially affects the lungs of patients, X-ray imaging of the chest is helpful for effective diagnosis. Any method for automatic, reliable, and accurate screening of COVID-19 infection would be beneficial for rapid detection and reducing medical or healthcare professional exposure to the virus. In the past, Convolutional Neural Networks (CNNs) proved to be quite successful in the classification of medical images. In this study, an automatic deep learning classification method for detecting COVID-19 from chest X-ray images is suggested using a CNN. A dataset consisting of 3616 COVID-19 chest X-ray images and 10,192 healthy chest X-ray images was used. The original data were then augmented to increase the data sample to 26,000 COVID-19 and 26,000 healthy X-ray images. The dataset was enhanced using histogram equalization, spectrum, grays, cyan and normalized with NCLAHE before being applied to CNN models. Initially using the dataset, the symptoms of COVID-19 were detected by employing eleven existing CNN models; VGG16, VGG19, MobileNetV2, InceptionV3, NFNet, ResNet50, ResNet101, DenseNet, EfficientNetB7, AlexNet, and GoogLeNet. From the models, MobileNetV2 was selected for further modification to obtain a higher accuracy of COVID-19 detection. Performance evaluation of the models was demonstrated using a confusion matrix. It was observed that the modified MobileNetV2 model proposed in the study gave the highest accuracy of 98% in classifying COVID-19 and healthy chest X-rays among all the implemented CNN models. The second-best performance was achieved from the pre-trained MobileNetV2 with an accuracy of 97%, followed by VGG19 and ResNet101 with 95% accuracy for both the models. The study compares the compilation time of the models. The proposed model required the least compilation time with 2 h, 50 min and 21 s. Finally, the Wilcoxon signed-rank test was performed to test the statistical significance. The results suggest that the proposed method can efficiently identify the symptoms of infection from chest X-ray images better than existing methods.

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“…The decision tree is the most popular supervised learning algorithm for prediction. As the name suggests, the algorithm is formed in a tree structure with the root node, branches and leaf nodes that indicate attributes, conditions and outcomes respectively [14]. Entropy as denoted in (1) shows the homogeneity as well as the purity of a dataset, and information gain is the change in an input's entropy, which is usually a reduction [15].…”

Section: Data Classification a Decision Treementioning

confidence: 99%

Supervised machine learning based liver disease prediction approach with LASSO feature selection

et al. 2021

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In this contemporary era, the uses of machine learning techniques are increasing rapidly in the field of medical science for detecting various diseases such as liver disease (LD). Around the globe, a large number of people die because of this deadly disease. By diagnosing the disease in a primary stage, early treatment can be helpful to cure the patient. In this research paper, a method is proposed to diagnose the LD using supervised machine learning classification algorithms, namely logistic regression, decision tree, random forest, AdaBoost, KNN, linear discriminant analysis, gradient boosting and support vector machine (SVM). We also deployed a least absolute shrinkage and selection operator (LASSO) feature selection technique on our taken dataset to suggest the most highly correlated attributes of LD. The predictions with 10 fold cross-validation (CV) made by the algorithms are tested in terms of accuracy, sensitivity, precision and f1-score values to forecast the disease. It is observed that the decision tree algorithm has the best performance score where accuracy, precision, sensitivity and f1-score values are 94.295%, 92%, 99% and 96% respectively with the inclusion of LASSO. Furthermore, a comparison with recent studies is shown to prove the significance of the proposed system.

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Deep Learning Predictive Model for Colon Cancer Patient using CNN-based Classification

Cited by 43 publications

References 16 publications

[Retracted] Hyperspectral Image Analysis of Colon Tissue and Deep Learning for Characterization of Health care

[Retracted] Hyperspectral Image Analysis of Colon Tissue and Deep Learning for Characterization of Health care

COVID-19 Detection Using Deep Learning Algorithm on Chest X-ray Images

Supervised machine learning based liver disease prediction approach with LASSO feature selection

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