Breast Cancer Histopathological Image Classification Using Stochastic Dilated Residual Ghost Model

Kashyap, Ramgopal

doi:10.4018/ijirr.289655

Cited by 31 publications

(23 citation statements)

References 42 publications

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“…Human physical and mental functions have been extensively studied using machine learning [ 35 – 41 ]. Industry stakeholders are requesting more openness when machine learning algorithms are used to provide crucial forecasts [ 42 ].…”

Section: Introductionmentioning

confidence: 99%

Large-Scale Textual Datasets and Deep Learning for the Prediction of Depressed Symptoms

Chakraborty¹,

Mahdi

Al-Abyadh

et al. 2022

Computational Intelligence and Neuroscience

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Millions of people worldwide suffer from depression. Assessing, treating, and preventing recurrence requires early detection of depressive symptoms as depression-related datasets expand and machine learning improves, intelligent approaches to detect depression in written material may emerge. This study provides an effective method for identifying texts describing self-perceived depressive symptoms by using long short-term memory (LSTM) based recurrent neural networks (RNN). On a huge dataset of a suicide and depression detection dataset taken from Kaggle with 233337 datasets, this information channel featured text-based teen questions. Then, using a one-hot technique, medical and psychiatric practitioners extract strong features from probably depressed symptoms. The characteristics outperform the usual techniques, which rely on word frequencies rather than symptoms to explain the underlying events in text messages. Depression symptoms can be distinguished from nondepression signals by using a deep learning system (nondepression posts). Eventually, depression is predicted by the RNN. In the suggested technique, the frequency of depressive symptoms outweighs their specificity. With correct annotations and symptom-based feature extraction, the method may be applied to different depression datasets. Because of this, chatbots and depression prediction can work together.

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Section: Introductionmentioning

confidence: 99%

Large-Scale Textual Datasets and Deep Learning for the Prediction of Depressed Symptoms

Chakraborty¹,

Mahdi

Al-Abyadh

et al. 2022

Computational Intelligence and Neuroscience

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“…Meanwhile, confusion matrices achieved by SoACNet are also drawn in Figure 4. Moreover, we also compare SoACNet with five representative CNN-related models [14,20,23,25,30] in terms of precision and recall indexes, and the comparative results are tabled in Table 5. As shown in this table, SoACNet outperforms the five models on all the four data sets under the precision index.…”

Section: Precision Recall and F1-score Resultsmentioning

confidence: 99%

“…Instead of using a single backbone, Kallipolitis et al [23] integrate three different pre-trained EfficientNets to achieve better classification performance for this medical task. Another proposition is to employ representative units to construct novel networks and classify pathological images in an end-to-end manner [4,13,[25][26][27][28][29][30]. For example, Bayramoglu et al [13] propose two different CNN networks that can directly benefit from the additional training data.…”

Section: Introductionmentioning

confidence: 99%

“…Then, Budak et al [29] propose a trainable model by combining fully convolutional network (FCN) with bi-directional long and short-term memory (Bi-LSTM) for the classification of breast cancer histopathology images, achieving an average accuracy of 94.98% on the BreakHis database. Additionally, Kashyap [25] combines the multi-scale stochastic dilated convolution model with the ghost unit (SDRG) to solve this medical classification problem, which well removes feature redundancy and reduces model complexity. Inspired by the great success of attention mechanisms, Zhu et al [28] introduce the squeeze-excitation-pruning block to capture more robust deep features of breast cancer histopathology images.…”

Section: Introductionmentioning

confidence: 99%

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Second‐order asymmetric convolution network for breast cancer histopathology image classification

Hou

Wang

et al. 2022

Journal of Biophotonics

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Recently, convolutional neural networks (CNNs) have been widely utilized for breast cancer histopathology image classification. Besides, research works have also convinced that deep high-order statistic models obviously outperform corresponding first-order counterparts in vision tasks. Inspired by this, we attempt to explore global deep high-order statistics to distinguish breast cancer histopathology images. To further boost the classification performance, we also integrate asymmetric convolution into the second-order network and propose a novel second-order asymmetric convolution network (SoACNet). SoACNet adopts a series of asymmetric convolution blocks to replace each stand square-kernel convolutional layer of the backbone architecture, followed by a global covariance pooling to compute second-order statistics of deep features, leading to a more robust representation of histopathology images. Extensive experiments on the public BreakHis dataset demonstrate the effectiveness of SoACNet for breast cancer histopathology image classification, which achieves competitive performance with the state-ofthe-arts.

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“…A modified residual neural network [24] has been proposed for breast cancer detection from histopathology images using modified ResNet34 and modified ResNet50 models. A Stochastic Dilated Residual Ghost (SDRG) method [25] has been proposed for cancer detection from breast histopathology images. In recent work, the combination of the attention technique and residual CNN model [26] has been utilized for breast cancer detection.…”

Section: Literature Surveymentioning

confidence: 99%

Design of ensemble recurrent model with stacked fuzzy ARTMAP for breast cancer detection

Das

Mohanty

2022

ACI

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PurposeIn time and accurate detection of cancer can save the life of the person affected. According to the World Health Organization (WHO), breast cancer occupies the most frequent incidence among all the cancers whereas breast cancer takes fifth place in the case of mortality numbers. Out of many image processing techniques, certain works have focused on convolutional neural networks (CNNs) for processing these images. However, deep learning models are to be explored well.Design/methodology/approachIn this work, multivariate statistics-based kernel principal component analysis (KPCA) is used for essential features. KPCA is simultaneously helpful for denoising the data. These features are processed through a heterogeneous ensemble model that consists of three base models. The base models comprise recurrent neural network (RNN), long short-term memory (LSTM) and gated recurrent unit (GRU). The outcomes of these base learners are fed to fuzzy adaptive resonance theory mapping (ARTMAP) model for decision making as the nodes are added to the F_2ˆa layer if the winning criteria are fulfilled that makes the ARTMAP model more robust.FindingsThe proposed model is verified using breast histopathology image dataset publicly available at Kaggle. The model provides 99.36% training accuracy and 98.72% validation accuracy. The proposed model utilizes data processing in all aspects, i.e. image denoising to reduce the data redundancy, training by ensemble learning to provide higher results than that of single models. The final classification by a fuzzy ARTMAP model that controls the number of nodes depending upon the performance makes robust accurate classification.Research limitations/implicationsResearch in the field of medical applications is an ongoing method. More advanced algorithms are being developed for better classification. Still, the scope is there to design the models in terms of better performance, practicability and cost efficiency in the future. Also, the ensemble models may be chosen with different combinations and characteristics. Only signal instead of images may be verified for this proposed model. Experimental analysis shows the improved performance of the proposed model. This method needs to be verified using practical models. Also, the practical implementation will be carried out for its real-time performance and cost efficiency.Originality/valueThe proposed model is utilized for denoising and to reduce the data redundancy so that the feature selection is done using KPCA. Training and classification are performed using heterogeneous ensemble model designed using RNN, LSTM and GRU as base classifiers to provide higher results than that of single models. Use of adaptive fuzzy mapping model makes the final classification accurate. The effectiveness of combining these methods to a single model is analyzed in this work.

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Breast Cancer Histopathological Image Classification Using Stochastic Dilated Residual Ghost Model

Cited by 31 publications

References 42 publications

Large-Scale Textual Datasets and Deep Learning for the Prediction of Depressed Symptoms

Large-Scale Textual Datasets and Deep Learning for the Prediction of Depressed Symptoms

Second‐order asymmetric convolution network for breast cancer histopathology image classification

Design of ensemble recurrent model with stacked fuzzy ARTMAP for breast cancer detection

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