<scp>DHS‐CapsNet</scp>: Dual horizontal squash capsule networks for lung and colon cancer classification from whole slide histopathological images

Adu, Kwabena; Yu, Yongbin; Cai, Jingye; Owusu-Agyemang, Kwabena; Twumasi, Baidenger Agyekum; Wang, Xiangxiang

doi:10.1002/ima.22569

Cited by 40 publications

(17 citation statements)

References 50 publications

(91 reference statements)

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“…The mathematical representations of MCC and FDR are represented in the Eqs. ( 10) and (11). Where, FP, FN, TP, and TN are represented as false positive, false negative, true positive, and true negative.…”

Section: Resultsmentioning

confidence: 99%

“…The presented model decreases the dataset noise and improves the classification performance, but it includes the issue of dimension disasters. K. Adu [11] implemented a new dual horizontal squash capsule networks (DHS-CapsNets) for classifying the colon and lung cancers on histopathology images. The presented DHS-CapsNets model comprises of two major functions in image classification: (i) horizontal squash (H-squash) function and encoder feature fusion.…”

Section: Related Workmentioning

confidence: 99%

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Histopathological Lung Cancer Detection Using Enhanced Grasshopper Optimization Algorithm with Random Forest

2022

IJIES

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In the recent decades, the lung cancer is the most dangerous disease that causes 1.6 million deaths per year. Therefore, the early recognition of lung cancer is an effective way to reduce the mortality rate. Compared to other imaging techniques, the histopathology images are effective in identifying the location and level of cancer. In this manuscript, a novel model is implemented for an automatic classification of histopathological images related to lung tissues. Initially, the color normalization technique is applied for improving the contrast of the histopathological images, which are acquired from the LC25000 lung histopathological image dataset. Additionally, the cancer segmentation is accomplished utilizing saliency driven region edge based top down level set (SDREL). Further, the feature descriptors: Alexnet and gray level co-occurrence matrix (GLCM) features were used for extracting the feature vectors from the segmented histopathology images. Lastly, the enhanced grasshopper optimization algorithm (EGOA) and random forest classifier were used for optimal feature selection and lung tissue classification. The simulation result shows that the EGOA-random forest model obtained 98.50 % of accuracy on the LC25000 lung histopathological image dataset.

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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Histopathological Lung Cancer Detection Using Enhanced Grasshopper Optimization Algorithm with Random Forest

2022

IJIES

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“…Li et al [ 131 ] proposed a definition of a new hybrid supervised learning method combining pixel-level and image-level annotations and achieved exceptional performance (sensitivity, 1.0000; AUC, 0.9906) for the recognition of GC using the ResNet-34 and Otsu methods. Kwabena et al [ 132 ] proposed the use of dual horizontal squash capsule networks for the identification of malignant and benign regions in CRC and obtained a high AUC (0.998). Chen et al [ 133 ] proposed a new loss function, namely rectified cross-entropy and upper transition loss for the prediction of CRC tumour grade and obtained an average accuracy of 0.76.…”

Section: Deep Learning In Gi Cancer Diagnosismentioning

confidence: 99%

Current Developments of Artificial Intelligence in Digital Pathology and Its Future Clinical Applications in Gastrointestinal Cancers

Wong

Leung

et al. 2022

Cancers

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The implementation of DP will revolutionize current practice by providing pathologists with additional tools and algorithms to improve workflow. Furthermore, DP will open up opportunities for development of AI-based tools for more precise and reproducible diagnosis through computational pathology. One of the key features of AI is its capability to generate perceptions and recognize patterns beyond the human senses. Thus, the incorporation of AI into DP can reveal additional morphological features and information. At the current rate of AI development and adoption of DP, the interest in computational pathology is expected to rise in tandem. There have already been promising developments related to AI-based solutions in prostate cancer detection; however, in the GI tract, development of more sophisticated algorithms is required to facilitate histological assessment of GI specimens for early and accurate diagnosis. In this review, we aim to provide an overview of the current histological practices in AP laboratories with respect to challenges faced in image preprocessing, present the existing AI-based algorithms, discuss their limitations and present clinical insight with respect to the application of AI in early detection and diagnosis of GI cancer.

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“…The therapy strategy will be more effective as a result of this as well. Furthermore, (Adu et al, 2021) offered the DHS-CapsNet, a revolutionary dual horizontal squash capsule network for classifying lung and colon tumors on histopathological pictures. Encoder feature fusion (EFF) and a unique horizontal squash (HSquash) algorithm were used to build DHS-CapsNet.…”

Section: Lung and Colon Cancer Using Hybrid Approachmentioning

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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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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DHS‐CapsNet: Dual horizontal squash capsule networks for lung and colon cancer classification from whole slide histopathological images

Cited by 40 publications

References 50 publications

Histopathological Lung Cancer Detection Using Enhanced Grasshopper Optimization Algorithm with Random Forest

Histopathological Lung Cancer Detection Using Enhanced Grasshopper Optimization Algorithm with Random Forest

Current Developments of Artificial Intelligence in Digital Pathology and Its Future Clinical Applications in Gastrointestinal Cancers

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

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