Colon Cancer Prediction On Different Magnified Colon Biopsy Images

Babu, Tina; Gupta, Deepa; Singh, Tripty; Hameed, Shahin

doi:10.1109/icoac44903.2018.8939067

Cited by 29 publications

(10 citation statements)

References 13 publications

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“…The authors used CNN and DFCNet in their study and tested their model on six different datasets. Babu et al described an RF-based classification model to predict the presence of colon cancer by analyzing histopathological cancer images [ 27 ]. First, they took the R-G-B images to the HSV plane and then performed wavelet decomposition to extract features.…”

Section: Related Workmentioning

confidence: 99%

A Machine Learning Approach to Diagnosing Lung and Colon Cancer Using a Deep Learning-Based Classification Framework

Masud

Sikder

Nahid

et al. 2021

Sensors

257

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The field of Medicine and Healthcare has attained revolutionary advancements in the last forty years. Within this period, the actual reasons behind numerous diseases were unveiled, novel diagnostic methods were designed, and new medicines were developed. Even after all these achievements, diseases like cancer continue to haunt us since we are still vulnerable to them. Cancer is the second leading cause of death globally; about one in every six people die suffering from it. Among many types of cancers, the lung and colon variants are the most common and deadliest ones. Together, they account for more than 25% of all cancer cases. However, identifying the disease at an early stage significantly improves the chances of survival. Cancer diagnosis can be automated by using the potential of Artificial Intelligence (AI), which allows us to assess more cases in less time and cost. With the help of modern Deep Learning (DL) and Digital Image Processing (DIP) techniques, this paper inscribes a classification framework to differentiate among five types of lung and colon tissues (two benign and three malignant) by analyzing their histopathological images. The acquired results show that the proposed framework can identify cancer tissues with a maximum of 96.33% accuracy. Implementation of this model will help medical professionals to develop an automatic and reliable system capable of identifying various types of lung and colon cancers.

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Section: Related Workmentioning

confidence: 99%

A Machine Learning Approach to Diagnosing Lung and Colon Cancer Using a Deep Learning-Based Classification Framework

Masud

Sikder

Nahid

et al. 2021

Sensors

257

View full text Add to dashboard Cite

show abstract

“…The authors utilized AlexNet, a well-known CNN-based architecture, for classification, which resulted in a classification accuracy of 91.47 %. In [12], Babu et al presented an RF-based classification algorithm for predicting the existence of colon cancer based on histological cancer images. First, the R-G-B images are transferred to the HSV plane.…”

Section: Related Workmentioning

confidence: 99%

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

Tasnim¹,

Chakraborty²,

Shamrat³

et al. 2021

IJACSA

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In recent years, the area of Medicine and Healthcare has made significant advances with the assistance of computational technology. During this time, new diagnostic techniques were developed. Cancer is the world's second-largest cause of mortality, claiming the lives of one out of every six individuals. The colon cancer variation is the most frequent and lethal of the numerous kinds of cancer. Identifying the illness at an early stage, on the other hand, substantially increases the odds of survival. A cancer diagnosis may be automated by using the power of Artificial Intelligence (AI), allowing us to evaluate more cases in less time and at a lower cost. In this research, CNN models are employed to analyse imaging data of colon cells. For colon cell image classification, CNN with max pooling and average pooling layers and MobileNetV2 models are utilized. To determine the learning rate, the models are trained and evaluated at various Epochs. It's found that the accuracy of the max pooling and average pooling layers is 97.49% and 95.48%, respectively. And MobileNetV2 outperforms the other two models with the most remarkable accuracy of 99.67% with a data loss rate of 1.24.

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“…In addition, the segmentation parameters for each microscope magnifications (4X, 5X, 10X, and 40X) were optimized by Saima et al [10] for ellipse fitting with the use of genetic algorithms, and the extracted gray-level co-occurrence matrices (GLCM) and gray-level histograms of the segmented region of interest (ROI), to classify colon biopsy image with the SVM classifier, reaching an average accuracy of 92.33%. A range of magnified colon (10X, 20X, 40X) images have been studied and classed with multi-classifier models in [11][12][13][14] for cancer detection, texture, wavelet, and shape features. Abdulhay et al [15] proposed a blood leukocyte segmentation strategy using static microscopes to categorize 100 distinct (72-abnormal, 38-normal) magnified microscopic images by SVM for tuning the segmentation parameters and filtering of the non-ROI image with the use of texture and local binary patterns and 95.3% accuracy was achieved.…”

Section: Introductionmentioning

confidence: 99%

Optimized cancer detection on various magnified histopathological colon images based on DWT features and FCM clustering

BABU,

SINGH,

GUPTA

et al. 2022

Turkish Journal of Electrical Engineering and Computer Sciences

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Due to the morphological characteristics and other biological aspects in histopathological images, the computerized diagnosis of colon cancer in histopathology images has gained popularity. The images acquired using the histopathology microscope may differ for greater visibility by magnifications. This causes a change in morphological traits leading to intra and inter-observer variability. An automatic colon cancer diagnosis system for various magnification is therefore crucial. This work proposes a magnification independent segmentation approach based on the connected component area and Double Density Dual Tree DWT (Discrete Wavelet Transform) coefficients are derived from the segmented region. The derived features are reduced further shortened with Fuzzy c-means. Further, with the aid of Artificial Neural Network (ANN) optimized with Salp Swarm Optimization (SSO), images are classified into normal and abnormal ones. This magnification independent proposed framework is evaluated across four different datasets (two real-time datasets and two public datasets) with different magnifications and the outcomes of all datasets were substantial when compared with the existing techniques. The proposed framework has shown strong concordance for cancer detection and can assist pathologists with a second opinion.

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Colon Cancer Prediction On Different Magnified Colon Biopsy Images

Cited by 29 publications

References 13 publications

A Machine Learning Approach to Diagnosing Lung and Colon Cancer Using a Deep Learning-Based Classification Framework

A Machine Learning Approach to Diagnosing Lung and Colon Cancer Using a Deep Learning-Based Classification Framework

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

Optimized cancer detection on various magnified histopathological colon images based on DWT features and FCM clustering

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