Classification with respect to colon adenocarcinoma and colon benign tissue of colon histopathological images with a new <scp>CNN</scp> model: MA_ColonNET

Yıldırım, Muhammed; Çınar, Ahmet

doi:10.1002/ima.22623

Cited by 49 publications

(21 citation statements)

References 35 publications

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“…Its improvement offers innovative opportunities for cancer screening instantaneously, with objectivity as well as accuracy. Yildirim and Cinar (2022) created a new method for identifying colon cancer images called MA ColonNET. The algorithm reached a 99.75 cent accuracy rate for detecting and classifying colon cancer which will help to avoid human errors that are common in older methods.…”

Section: Lung and Colon Cancer Using Hybrid Approachmentioning

confidence: 99%

“…It is one of the major drivers of fatality globally, although its impact is not evenly distributed With the advancements, the amount of data stored in archives is growing by the day (Godkhindi and Gowda, 2017). It is extremely challenging to analyze a huge quantity of data and extrapolate using traditional approaches (Yildirim and Cinar, 2022). The increased availability of healthcare data provides scientists with a fresh opportunity to improve existing approaches for further comprehensive clinical analysis (Sarwinda et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning-based Lung and Colon Cancer Detection using Deep Feature Extraction and Ensemble Learning

Talukder¹,

Islam²,

Uddin³

et al. 2022

Preprint

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Cancer is a fatal disease caused by a combination of genetic diseases and a variety of biochemical abnormalities. Lung and colon cancer have emerged as two of the leading causes of death and disability in humans. The histopathological detection of such malignancies is usually the most important component in determining the best course of action.Early detection of the ailment on either front considerably decreases the likelihood of mortality. Machine learning and deep learning techniques can be utilized to speed up such cancer detection, allowing researchers to study a large number of patients in a much shorter amount of time and at a lower cost. In this research work, we introduced a hybrid ensemble feature extraction model to efficiently identify lung and colon cancer. It integrates deep feature extraction and ensemble learning with high-performance filtering for cancer image datasets. The model is evaluated on histopathological (LC25000) lung and colon datasets. According to the study findings, our hybrid model can detect lung, colon, and (lung and colon) cancer with accuracy rates of 99.05%, 100%, and 99.30%, respectively. The study's findings show that our proposed strategy outperforms existing models significantly. Thus, these models could be applicable in clinics to support the doctor in the diagnosis of cancers.

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Section: Lung and Colon Cancer Using Hybrid Approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Machine Learning-based Lung and Colon Cancer Detection using Deep Feature Extraction and Ensemble Learning

Talukder¹,

Islam²,

Uddin³

et al. 2022

Preprint

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“…Moreover, DL based approaches are actively employed for pattern classification or analysis of the medical images. There are extensive studies conducted with numerous methods focusing on the individual diagnosis of colorectal cancer from histopathology images, such as classification of colorectal adenocarcinoma [ [2], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15]], colon polyp classification [ [16], [17], [4], [18], [19], [20], [21]] and colon gland classification [ [5], [22], [23]].…”

Section: Introductionmentioning

confidence: 99%

“…They evaluate the performance of the system by using custom collected WSIs and The Cancer Genome Atlas (TCGA) database, and overall area under the receiver operating characteristic curve (AUROC) of 0.9746 is achieved. Yildirim et al [14] suggest a CNN based network for detection of the colon cancer from WSI. Zhou et al [15] employ global labels to localize the cancerous regions from the colonic WSIs.…”

Section: Introductionmentioning

confidence: 99%

Classification of Colorectal Polyps from Histopathological Images using Ensemble of ConvNeXt Variants

Tasdemir

Akay

Doğan

et al. 2022

Preprint

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In this work, we propose a computer-aided diagnosis system for automatic classification of adenomatous polyps from colonic histological images. The proposed method employs an ensemble of ConvNeXt variants, which is one of the most recent and prominent convolution based deep learning architectures. Comprehensive experiments show that classification performance of the proposed method outperforms the state-of-the-art Deep CNN models on our dataset, with a 95% accuracy. It achieves 91.1% and 90% classification accuracy respectively on EBHI and UniToPatho datasets, which shows that the model can generalize well for other datasets as well. Furthermore, we investigate the effect of stain normalization preprocessing on the classification accuracy. While the other methods are sensitive to the normalization, the proposed method retains its accuracy levels under the variations, which also indicates a high generalization ability. Lastly, we use the Grad-Cam method and show that the proposed model gives more attention to the regions where the cancer indicators potentially reside.

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“…Deep learning-based approaches have recently found widespread use in the medical field. [13][14][15][16] A deep learningbased model is proposed to overcome the difficulties mentioned in the classification of eardrum otoendoscopic images. With the proposed model, an average accuracy of 94.27% was obtained.…”

Section: Introductionmentioning

confidence: 99%

Automatic detection of eardrum otoendoscopic images in patients with otitis media using hybrid‐based deep models

Eroğlu

Yıldırım

2021

Int J Imaging Syst Tech

Self Cite

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Otitis media with effusion (OME) is fluid accumulation in the middle ear without signs of systemic infection. OME can cause hearing loss, ear fullness, speech retardation, and a decrease in social relations and school success. In the treatment of OME, methods such as medical treatment and placing a tympanostomy tube in the eardrum are used. Correct evaluation of the eardrum is necessary to diagnose OME and follow‐up properly in the following period. In this study, we aimed to evaluate the otoendoscopic images of the eardrum in patients with otitis media with effusion using deep models. In the proposed model, Efficientnetb0, Darknet53, and Densenet201 architectures are used as the base. The feature maps obtained using these architectures are combined. In this way, different features of the otoendoscopic images of the eardrum in the data set were extracted and combined using different architectures. neighborhood component analysis optimization technique is used to select important features from the combined feature maps. By using this optimization method, the size of feature maps is reduced. In this way, the model is trained faster, and the testing process is carried out in a shorter time. When these features are classified in different classifiers, the model we propose has the most successful average accuracy value of 94.27% in the SVM classifier.

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Classification with respect to colon adenocarcinoma and colon benign tissue of colon histopathological images with a new CNN model: MA_ColonNET

Cited by 49 publications

References 35 publications

Machine Learning-based Lung and Colon Cancer Detection using Deep Feature Extraction and Ensemble Learning

Machine Learning-based Lung and Colon Cancer Detection using Deep Feature Extraction and Ensemble Learning

Classification of Colorectal Polyps from Histopathological Images using Ensemble of ConvNeXt Variants

Automatic detection of eardrum otoendoscopic images in patients with otitis media using hybrid‐based deep models

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