Lung and colon cancer detection with convolutional neural networks on histopathological images

Wahid, Radical Rakhman; Nisa, Chilyatun; Amaliyah, Rahayu Prabawati; Puspaningrum, Eva Yulia

doi:10.1063/5.0114327

Cited by 27 publications

(10 citation statements)

References 11 publications

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“…In their work, Wahid et al [ 14 ] utilized three pre-trained Convolutional Neural Network (CNN) models: ResNet18, GoogLeNet, and ShuffleNet V2. They also employed the LC25000 dataset and a straightforward modified CNN model.…”

Section: Related Workmentioning

confidence: 99%

“…Tested only on one dataset. Hadiyoso et al [ 13 ] VGG16 with CLAHE LC25000 Image enhancement 98.96 Wahid et al [ 14 ] Various CNNs LC25000 Multiple models Up to 99.87 Iqbal et al [ 15 ] ColonNet LC25000 High F1-score 96.31 Al Ghamdi et al [ 16 ] ShuffleNet, DCRNN LC25000 Transfer learning 98.99 Stephen and Sain [ 17 ] Neural Arch Search LC25000 Neural net search 93.91 Kumar et al [ 18 ] Transfer Learning LC25000 Feature extraction 98.60 Hage Chehade et al [ 19 ] Various ML models LC25000 Multiple models Up to 99 Mridha et al [ 20 ] DL Framework LC25000 5 types of cells 98.30 Mehmood et al [ 21 ] Modified AlexNet LC25000 Fine-tuned model 98.40 Provat et al [ 22 ] Custom CNN LC25000 Cyclic learning rates ...…”

Section: Related Workmentioning

confidence: 99%

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

Uddin,

Chen,

Akter

et al. 2024

Heliyon

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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

Uddin,

Chen,

Akter

et al. 2024

Heliyon

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“…Reference Limitation [9] accuracy 71% [10] accuracy 84.02%, specificity 85.34%, sensitivity 82.71% [11] no performance result, the dataset only 200 images binary classification and machine learning, [12] dataset using patches of images, with an accuracy 75.41% [13] accuracy 87.16% [14] accuracy 98.82%…”

Section: Table 1 Limitations Of Previous Studiesmentioning

confidence: 99%

“…Further research by Wahid et al [14] using pre-trained CNN models: shuffleNet-v2, googlenet, and resnet18. Dataset from LC25000.…”

Section: Introductionmentioning

confidence: 99%

Inception-v3 with reduce learning rate for optimization of lung cancer histopathology classification

Setiawan,

Suhadi,

Pramudita

et al. 2024

ISI

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Cancer is an uncontrolled and destructive proliferation of body cells. Lung cancer is the highest cause of death in Indonesia, especially among men. The cancer patient can be examined for histopathological checkups. This examination is carried out by taking body tissue at a place where cancer cells are suspected. Histopathology is the gold standard for detecting pathology or abnormalities in body cells. The results of a histopathological examination can differentiate between normal body cells, cancer cells, and their types. There are three types of lung cancer histopathology: adenocarcinomas, squamous cell carcinomas, and benign lung tissues. Classification of lung cancer using histopathology images is an alternative to detecting the severity of cancer. This study used Deep Learning Convolutional Neural Network (CNN). Transfer learning utilizes ImageNet weights and biases from the Inception-v3 pre-trained network, so a new model is not trained from scratch. The hyperparameter uses a learning rate (LR) of 0.0001, epoch 50, batch-size 32, and RMSProp optimization. In addition, there is tuning with reduced lr when there is an increase in validation loss before reaching the maximum epoch. The dataset uses the LC25000. The data consists of 3,000 images, three classes with 1,000 classes per class. The best results show accuracy, precision, and recall are 99.17%, 99.17%, and 99%, respectively. Performance increased by 3% compared to the baseline method without learning rate tuning.

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“…Lung disorders are becoming the world's second leading cause of death. The acute and chronic respiratory conditions that directly impact individuals' health include COPD, asthma, lung inflammation, infectious diseases, tuberculosis, pulmonary embolism, sleep apnea, and occupational lung diseases [2,[14][15][16][17][18][19][20][21][22][23][24][25][26].…”

Section: Introductionmentioning

confidence: 99%

Transfer Learning based Diagnosis and Analysis of Lung Sound Aberrations

Gulzar¹,

Li²,

Manzoor³

et al. 2023

IJBB

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With the development of computer -systems that can collect and analyze enormous volumes of data, the medical profession is establishing several non-invasive tools. This work attempts to develop a non-invasive technique for identifying respiratory sounds acquired by a stethoscope and voice recording software via machine learning techniques. This study suggests a trained and proven CNN-based approach for categorizing respiratory sounds. A visual representation of each audio sample is constructed, allowing resource identification for classification using methods like those used to effectively describe visuals. We used a technique called MelFrequency Cepstral Coefficients (MFCCs). Here, features are retrieved and categorized via VGG16 (transfer learning) and prediction is accomplished using 5-fold cross-validation. Employing various data splitting techniques, Respiratory Sound Database obtained cutting- edge results, including accuracy of 95%, precision of 88%, recall score of 86%, and F1 score of 81%. The ICBHI dataset is used to train and test the model.

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Lung and colon cancer detection with convolutional neural networks on histopathological images

Cited by 27 publications

References 11 publications

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

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

Inception-v3 with reduce learning rate for optimization of lung cancer histopathology classification

Transfer Learning based Diagnosis and Analysis of Lung Sound Aberrations

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