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

Masud, Mehedi; Sikder, Niloy; Nahid, Abdullah-Al; AlZain, Mohammed A.

doi:10.3390/s21030748

Cited by 257 publications

(101 citation statements)

References 44 publications

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“…The studies that use data produced from histopathology slides are the focus of this section since we are solely interested in data derived from histopathology slides in this study. Some authors focused entirely on lung cancer classification [42], while others primarily focused on colon cancer classification [43]. Researchers in recent works have attempted to classify images of lung and colon cancer at the same time.…”

Section: Related Workmentioning

confidence: 99%

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

Ali

2021

Diagnostics

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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.

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Section: Related Workmentioning

confidence: 99%

“…Masud et al [43] classify histopathological lung and colon images using a novel deep learning-based technique. They used domain transformations of two types to extract four sets of features for image classification.…”

Section: Related Workmentioning

confidence: 99%

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

Ali

2021

Diagnostics

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“…The approach was tested using data from the Asu Mayo Test Clinic database and obtained over 90% classification accuracy. Masud et al [16] inscribe a classification framework to distinguish colon tissues (two benign and three malignant) by evaluating their histological pictures using CNN and Digital Image Processing (DIP) methods. The obtained findings indicate that the proposed framework can detect cancer tissues with an accuracy of up to 96.33 %.…”

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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“…Finally, the contrast is increased at the edges, and the effect is applied to the original image. If we take a sample image, I A , its sharpened form I S can be calculated as follows [47]:…”

Section: Dealing With Uneven Sample Sizesmentioning

confidence: 99%

Severity Classification of Diabetic Retinopathy Using an Ensemble Learning Algorithm through Analyzing Retinal Images

et al. 2021

Self Cite

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Diabetic Retinopathy (DR) refers to the damages endured by the retina as an effect of diabetes. DR has become a severe health concern worldwide, as the number of diabetes patients is soaring uncountably. Periodic eye examination allows doctors to detect DR in patients at an early stage to initiate proper treatments. Advancements in artificial intelligence and camera technology have allowed us to automate the diagnosis of DR, which can benefit millions of patients indeed. This paper inscribes a novel method for DR diagnosis based on the gray-level intensity and texture features extracted from fundus images using a decision tree-based ensemble learning technique. This study primarily works with the Asia Pacific Tele-Ophthalmology Society 2019 Blindness Detection (APTOS 2019 BD) dataset. We undertook several steps to curate its contents to make them more suitable for machine learning applications. Our approach incorporates several image processing techniques, two feature extraction techniques, and one feature selection technique, which results in a classification accuracy of 94.20% (margin of error: ±0.32%) and an F-measure of 93.51% (margin of error: ±0.5%). Several other parameters regarding the proposed method’s performance have been presented to manifest its robustness and reliability. Details on each employed technique have been included to make the provided results reproducible. This method can be a valuable tool for mass retinal screening to detect DR, thus drastically reducing the rate of vision loss attributed to it.

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

Cited by 257 publications

References 44 publications

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

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

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

Severity Classification of Diabetic Retinopathy Using an Ensemble Learning Algorithm through Analyzing Retinal Images

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