Colon and lung cancer classification from multi-modal images using resilient and efficient neural network architectures

Uddin, A. Hasib; Chen, Yen-Lin; Akter, Miss Rokeya; Ku, Chin Soon; Yang, Jing; Por, Lip Yee

doi:10.1016/j.heliyon.2024.e30625

Cited by 6 publications

(1 citation statement)

References 52 publications

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“…Training results with input data produce train precision, val accuracy, train loss and val loss values [21]. This process uses a number of epochs of 25 and a learning rate of 0.001.…”

Section: Trainingmentioning

confidence: 99%

Developing a classification system for brain tumors using the ResNet152V2 CNN model architecture

Rhomadhon,

Ningtias

2024

J. Soft Comput. Explor.

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According to The American Cancer Society, in 2021 there were 24,530 cases of brain and nervous system tumors. The National Cancer Institute reports that there are approximately 4.4 new cases of brain tumors per 100,000 men and women per year. Brain tumors can be detected using magnetic resonance imaging (MRI), a scanning tool that uses a magnetic field and a computer to record brain images and is able to provide clear visualization of differences in soft tissue such as white matter and gray matter. However, this cannot be done optimally because it still relies on manual analysis, so it cannot classify brain tumor types on larger datasets with the potential for error and a low level of accuracy. To accurately determine the type of brain tumor, a better classification method is needed. The aim of this study is to determine the accuracy of brain tumor calcification using the deep learning model. In this study, the classification of brain tumor types was carried out using the ResNet152V2 convolutional neural network (CNN) model which has a depth of 152 layers. The dataset used in this study was 7,023 MRI images of brain tumors consisting of 1,645 meningiomas, 1,621 gliomas, 1,757 pituitary and 2,000 normal. Research results show an accuracy value of 94.44%, so it can be concluded that the ResNet152V2 model performs well in classifying brain tumor images and can be used as a medium for physicians to more accurately diagnose brain tumor patients more accurately.

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“…Training results with input data produce train precision, val accuracy, train loss and val loss values [21]. This process uses a number of epochs of 25 and a learning rate of 0.001.…”

Section: Trainingmentioning

confidence: 99%

Developing a classification system for brain tumors using the ResNet152V2 CNN model architecture

Rhomadhon,

Ningtias

2024

J. Soft Comput. Explor.

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Advancing cancer diagnosis and prognostication through deep learning mastery in breast, colon, and lung histopathology with ResoMergeNet

Ejiyi,

Qin,

Agbesi

et al. 2025

Computers in Biology and Medicine

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Exploring vision transformers and XGBoost as deep learning ensembles for transforming carcinoma recognition

Raju,

Venkatesh,

Padmaja

et al. 2024

Sci Rep

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Early detection of colorectal carcinoma (CRC), one of the most prevalent forms of cancer worldwide, significantly enhances the prognosis of patients. This research presents a new method for improving CRC detection using a deep learning ensemble with the Computer Aided Diagnosis (CADx). The method involves combining pre-trained convolutional neural network (CNN) models, such as ADaRDEV2I-22, DaRD-22, and ADaDR-22, using Vision Transformers (ViT) and XGBoost. The study addresses the challenges associated with imbalanced datasets and the necessity of sophisticated feature extraction in medical image analysis. Initially, the CKHK-22 dataset comprised 24 classes. However, we refined it to 14 classes, which led to an improvement in data balance and quality. This improvement enabled more precise feature extraction and improved classification results. We created two ensemble models: the first model used Vision Transformers to capture long-range spatial relationships in the images, while the second model combined CNNs with XGBoost to facilitate structured data classification. We implemented DCGAN-based augmentation to enhance the dataset’s diversity. The tests showed big improvements in performance, with the ADaDR-22 + Vision Transformer group getting the best results, with a testing accuracy of 93.4% and an AUC of 98.8%. In contrast, the ADaDR-22 + XGBoost model had an AUC of 97.8% and an accuracy of 92.2%. These findings highlight the efficacy of the proposed ensemble models in detecting CRC and highlight the importance of using well-balanced, high-quality datasets. The proposed method significantly enhances the clinical diagnostic accuracy and the capabilities of medical image analysis or early CRC detection.

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Colon and lung cancer classification from multi-modal images using resilient and efficient neural network architectures

Cited by 6 publications

References 52 publications

Developing a classification system for brain tumors using the ResNet152V2 CNN model architecture

Developing a classification system for brain tumors using the ResNet152V2 CNN model architecture

Advancing cancer diagnosis and prognostication through deep learning mastery in breast, colon, and lung histopathology with ResoMergeNet

Exploring vision transformers and XGBoost as deep learning ensembles for transforming carcinoma recognition

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