An improved multi-scale gradient generative adversarial network for enhancing classification of colorectal cancer histological images

Jiang, Liwen; Huang, Shuting; Luo, Chaofan; Zhang, Jiangyu; Chen, Wenjing; Liu, Zhenyu

doi:10.3389/fonc.2023.1240645

Cited by 3 publications

(2 citation statements)

References 33 publications

(28 reference statements)

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“…In [16] the number of epochs used was 200. [18] has the limitation of time-consuming processes as well as less accuracy. [19] and [20] shows the importance of finetuning and selecting hyperparameters.…”

Section: Related Workmentioning

confidence: 99%

Ensemble Residual Network with Iterative Randomized Hyperparameter Optimization for Colorectal Cancer Classification

S. Vimala

2024

jes

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The analysis of WSI images is widely acknowledged as a method, for identifying stages of cancer and evaluating the spread of cancer cells in tissues. In histopathology image analysis deep learning models are gaining increasing importance. To enhance the effectiveness of these models it is crucial to train and fine-tune Convolutional Neural Network algorithms by adjusting hyperparameters like batch size, convolution depth, and learning rate (LR). However, determining the hyperparameters can be challenging as they significantly impact model performance. This study examines how hyperparameters influence cancer classification, in histopathology images using the CNN architecture. A method called iterative randomized hyperparameter optimization is proposed, which gradually reduces variations over time by adjusting parameters after each network layer. The combination of hyperparameters is applied to version 1 of ResNet18, ResNet50, and ResNet101 models and version 2 of ResNet50, ResNet101, and ResNet151. The results are then combined using the Adaptive Boosting algorithm. The results are quite promising on ensemble version 1 residual networks, achieving an accuracy of 98.7% when tested on nine tissue classes.

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Section: Related Workmentioning

confidence: 99%

Ensemble Residual Network with Iterative Randomized Hyperparameter Optimization for Colorectal Cancer Classification

S. Vimala

2024

jes

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“…Table 1 provides an overview of manual analysis and AI-based studies from various literature sources. In a study by Jiang ( 14 ), a high accuracy rate of 0.89 was achieved using InceptionV3 Multi-Scale Gradients and Generative Adversarial Network for classifying colorectal cancer histopathological images. Kather et al.…”

Section: Introductionmentioning

confidence: 99%

Histopathology image classification: highlighting the gap between manual analysis and AI automation

Doğan,

Yılmaz

2024

Front. Oncol.

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The field of histopathological image analysis has evolved significantly with the advent of digital pathology, leading to the development of automated models capable of classifying tissues and structures within diverse pathological images. Artificial intelligence algorithms, such as convolutional neural networks, have shown remarkable capabilities in pathology image analysis tasks, including tumor identification, metastasis detection, and patient prognosis assessment. However, traditional manual analysis methods have generally shown low accuracy in diagnosing colorectal cancer using histopathological images. This study investigates the use of AI in image classification and image analytics using histopathological images using the histogram of oriented gradients method. The study develops an AI-based architecture for image classification using histopathological images, aiming to achieve high performance with less complexity through specific parameters and layers. In this study, we investigate the complicated state of histopathological image classification, explicitly focusing on categorizing nine distinct tissue types. Our research used open-source multi-centered image datasets that included records of 100.000 non-overlapping images from 86 patients for training and 7180 non-overlapping images from 50 patients for testing. The study compares two distinct approaches, training artificial intelligence-based algorithms and manual machine learning models, to automate tissue classification. This research comprises two primary classification tasks: binary classification, distinguishing between normal and tumor tissues, and multi-classification, encompassing nine tissue types, including adipose, background, debris, stroma, lymphocytes, mucus, smooth muscle, normal colon mucosa, and tumor. Our findings show that artificial intelligence-based systems can achieve 0.91 and 0.97 accuracy in binary and multi-class classifications. In comparison, the histogram of directed gradient features and the Random Forest classifier achieved accuracy rates of 0.75 and 0.44 in binary and multi-class classifications, respectively. Our artificial intelligence-based methods are generalizable, allowing them to be integrated into histopathology diagnostics procedures and improve diagnostic accuracy and efficiency. The CNN model outperforms existing machine learning techniques, demonstrating its potential to improve the precision and effectiveness of histopathology image analysis. This research emphasizes the importance of maintaining data consistency and applying normalization methods during the data preparation stage for analysis. It particularly highlights the potential of artificial intelligence to assess histopathological images.

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Color-CADx: a deep learning approach for colorectal cancer classification through triple convolutional neural networks and discrete cosine transform

Sharkas,

Attallah

2024

Sci Rep

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Colorectal cancer (CRC) exhibits a significant death rate that consistently impacts human lives worldwide. Histopathological examination is the standard method for CRC diagnosis. However, it is complicated, time-consuming, and subjective. Computer-aided diagnostic (CAD) systems using digital pathology can help pathologists diagnose CRC faster and more accurately than manual histopathology examinations. Deep learning algorithms especially convolutional neural networks (CNNs) are advocated for diagnosis of CRC. Nevertheless, most previous CAD systems obtained features from one CNN, these features are of huge dimension. Also, they relied on spatial information only to achieve classification. In this paper, a CAD system is proposed called “Color-CADx” for CRC recognition. Different CNNs namely ResNet50, DenseNet201, and AlexNet are used for end-to-end classification at different training–testing ratios. Moreover, features are extracted from these CNNs and reduced using discrete cosine transform (DCT). DCT is also utilized to acquire spectral representation. Afterward, it is used to further select a reduced set of deep features. Furthermore, DCT coefficients obtained in the previous step are concatenated and the analysis of variance (ANOVA) feature selection approach is applied to choose significant features. Finally, machine learning classifiers are employed for CRC classification. Two publicly available datasets were investigated which are the NCT-CRC-HE-100 K dataset and the Kather_texture_2016_image_tiles dataset. The highest achieved accuracy reached 99.3% for the NCT-CRC-HE-100 K dataset and 96.8% for the Kather_texture_2016_image_tiles dataset. DCT and ANOVA have successfully lowered feature dimensionality thus reducing complexity. Color-CADx has demonstrated efficacy in terms of accuracy, as its performance surpasses that of the most recent advancements.

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An improved multi-scale gradient generative adversarial network for enhancing classification of colorectal cancer histological images

Cited by 3 publications

References 33 publications

Ensemble Residual Network with Iterative Randomized Hyperparameter Optimization for Colorectal Cancer Classification

Ensemble Residual Network with Iterative Randomized Hyperparameter Optimization for Colorectal Cancer Classification

Histopathology image classification: highlighting the gap between manual analysis and AI automation

Color-CADx: a deep learning approach for colorectal cancer classification through triple convolutional neural networks and discrete cosine transform

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