An Explainable Classification Method Based on Complex Scaling in Histopathology Images for Lung and Colon Cancer

Tummala, Sudhakar; Kadry, Seifedine; Nadeem, Ahmed; Rauf, Hafiz Tayyab; Gul, Nadia

doi:10.3390/diagnostics13091594

Cited by 15 publications

(3 citation statements)

References 35 publications

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“…Most of the existing approaches have been tested on benchmark datasets [27,28], but it is unclear whether there are enough data to support their implementation in current evidence-based clinical practice [29]. Advanced studies reporting clinical trials have been conducted only for colon tissue or nucleus segmentation [30].…”

Section: Related Workmentioning

confidence: 99%

Convolutional Neural Networks in the Diagnosis of Colon Adenocarcinoma

Leo,

Carcagnì,

Signore

et al. 2024

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Colorectal cancer is one of the most lethal cancers because of late diagnosis and challenges in the selection of therapy options. The histopathological diagnosis of colon adenocarcinoma is hindered by poor reproducibility and a lack of standard examination protocols required for appropriate treatment decisions. In the current study, using state-of-the-art approaches on benchmark datasets, we analyzed different architectures and ensembling strategies to develop the most efficient network combinations to improve binary and ternary classification. We propose an innovative two-stage pipeline approach to diagnose colon adenocarcinoma grading from histological images in a similar manner to a pathologist. The glandular regions were first segmented by a transformer architecture with subsequent classification using a convolutional neural network (CNN) ensemble, which markedly improved the learning efficiency and shortened the learning time. Moreover, we prepared and published a dataset for clinical validation of the developed artificial neural network, which suggested the discovery of novel histological phenotypic alterations in adenocarcinoma sections that could have prognostic value. Therefore, AI could markedly improve the reproducibility, efficiency, and accuracy of colon cancer diagnosis, which are required for precision medicine to personalize the treatment of cancer patients.

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Section: Related Workmentioning

confidence: 99%

Convolutional Neural Networks in the Diagnosis of Colon Adenocarcinoma

Leo,

Carcagnì,

Signore

et al. 2024

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“…The accuracy rating for the EGOA (Enhanced Grasshopper Optimization Algorithm) with random forest model was 98.50%. EffcientNetV2 big, medium, and small models are a deep learning architecture built on the concepts of compound scaling and progressive learning [33]. Using the EffcientNetV2-L model for the 5-class categorization of lung and colon cancers, they attained an accuracy of 99.97% on the test set.…”

Section: Related Workmentioning

confidence: 99%

An Efficient Combination of Convolutional Neural Network and LightGBM Algorithm for Lung Cancer Histopathology Classification

et al. 2023

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The most dangerous disease in recent decades is lung cancer. The most accurate method of cancer diagnosis, according to research, is through the use of histopathological images that are acquired by a biopsy. Deep learning techniques have achieved success in bioinformatics, particularly medical imaging. In this paper, we present an innovative method for rapidly identifying and classifying histopathology images of lung tissues by combining a newly proposed Convolutional Neural Networks (CNN) model with a few total parameters and the enhanced Light Gradient Boosting Model (LightGBM) classifier. After the images have been pre-processed in this study, the proposed CNN technique is provided for feature extraction. Then, the LightGBM model with multiple threads has been used for lung tissue classification. The simulation result, applied to the LC25000 dataset, demonstrated that the novel technique successfully classifies lung tissue with 99.6% accuracy and sensitivity. Furthermore, the proposed CNN model has achieved the lowest training parameters of only one million parameters, and it has also achieved the shortest processing time of just one second throughout the feature extraction process. When this result is compared with the most recent state-of-the-art approaches, the suggested approach has increased effectiveness in the areas of both disease classification accuracy and processing time.

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“…Cancer can affect any organ of the human body; the foremost areas to be commonly affected are the brain, colon, skin, breasts, stomach, rectum, liver, prostate, and lungs. The common tumors to cause death in females and males are lung and colon cancer (LCC) [ 1 ]. When lung cells mutate uncontrollably, malignant cells appear, forming clusters called cancers.…”

Section: Introductionmentioning

confidence: 99%

Al-Biruni Earth Radius Optimization with Transfer Learning Based Histopathological Image Analysis for Lung and Colon Cancer Detection

AlGhamdi

Asar

Assiri

et al. 2023

Cancers

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An early diagnosis of lung and colon cancer (LCC) is critical for improved patient outcomes and effective treatment. Histopathological image (HSI) analysis has emerged as a robust tool for cancer diagnosis. HSI analysis for a LCC diagnosis includes the analysis and examination of tissue samples attained from the LCC to recognize lesions or cancerous cells. It has a significant role in the staging and diagnosis of this tumor, which aids in the prognosis and treatment planning, but a manual analysis of the image is subject to human error and is also time-consuming. Therefore, a computer-aided approach is needed for the detection of LCC using HSI. Transfer learning (TL) leverages pretrained deep learning (DL) algorithms that have been trained on a larger dataset for extracting related features from the HIS, which are then used for training a classifier for a tumor diagnosis. This manuscript offers the design of the Al-Biruni Earth Radius Optimization with Transfer Learning-based Histopathological Image Analysis for Lung and Colon Cancer Detection (BERTL-HIALCCD) technique. The purpose of the study is to detect LCC effectually in histopathological images. To execute this, the BERTL-HIALCCD method follows the concepts of computer vision (CV) and transfer learning for accurate LCC detection. When using the BERTL-HIALCCD technique, an improved ShuffleNet model is applied for the feature extraction process, and its hyperparameters are chosen by the BER system. For the effectual recognition of LCC, a deep convolutional recurrent neural network (DCRNN) model is applied. Finally, the coati optimization algorithm (COA) is exploited for the parameter choice of the DCRNN approach. For examining the efficacy of the BERTL-HIALCCD technique, a comprehensive group of experiments was conducted on a large dataset of histopathological images. The experimental outcomes demonstrate that the combination of AER and COA algorithms attain an improved performance in cancer detection over the compared models.

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An Explainable Classification Method Based on Complex Scaling in Histopathology Images for Lung and Colon Cancer

Cited by 15 publications

References 35 publications

Convolutional Neural Networks in the Diagnosis of Colon Adenocarcinoma

Convolutional Neural Networks in the Diagnosis of Colon Adenocarcinoma

An Efficient Combination of Convolutional Neural Network and LightGBM Algorithm for Lung Cancer Histopathology Classification

Al-Biruni Earth Radius Optimization with Transfer Learning Based Histopathological Image Analysis for Lung and Colon Cancer Detection

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