Combining the advantages of radiomic features based feature extraction and hyper parameters tuned RERNN using LOA for breast cancer classification

Subasree, S.; Sakthivel, N.; Tripathi, Khushboo; Agarwal, Deepshikha; Tyagi, Amit Kumar

doi:10.1016/j.bspc.2021.103354

Cited by 24 publications

(11 citation statements)

References 37 publications

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“…The performance of the proposed BCC‐DCNN‐KHO‐MI with MAMMOSET is estimated with existing methods, namely BCC‐Google Net‐MI, 25 BCC‐Visual Geometry Group Network‐MI, 25 and BCC‐Residual Networks‐MI 25 and BC‐DNN‐MI 22 . Then the performance of the proposed BCC‐DCNN‐KHO‐MI with real‐time dataset is estimated with existing methods, namely BCC‐Google Net‐MI, 25 BCC‐Visual Geometry Group Network‐MI, 25 and BCC‐Residual Networks‐MI, 25 BC‐RERNN‐LOA‐MI 27 and BC‐CNN‐MI 28 …”

Section: Resultsmentioning

confidence: 99%

“…Subasree et al 27 have suggested Recalling Enhanced Recurrent Neural Network (RERNN) based upon Lizard optimization Algorithm. The images were gathered from VPS hospital and pre‐processed using Altered Phase Preserving Dynamic Range Compression.…”

Section: Literature Surveymentioning

confidence: 99%

“…Medical detection of breast cancer is a challenging task of biomedical domain. Several methods were previously suggested in literature survey [25][26][27][28][29][30] to raise the efficiency of classification. The existing methods, such as Random Forest, k-Nearest-Neighbor, Naïve Bayes did not offer adequate exactness as well as increased the error rate.…”

Section: Research Gapmentioning

confidence: 99%

“…A numerous breast cancer classification approaches were suggested in literature survey, [25][26][27][28][29][30] those approaches have certain limitations, like Low detection rate for benign samples analyzed to high fraction of malignant samples, during categorization procedure the exactness of Benign is obviously categorize, also lessens the malignant exactness, a few approaches did not categorize the malignant and Benign clearly, they only show the normal region accuracy and increased the error rate, a few more methods show image accuracy but raised the computational time. To devastate these weaknesses, some resolutions necessitate to be laid forward.…”

Section: Research Gapmentioning

confidence: 99%

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Krill herd optimization algorithm with deep convolutional neural network fostered breast cancer classification using mammogram images

Bai

Krish

2023

Concurrency and Computation

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In this paper proposes a Krill Herd Optimization algorithm with Deep Convolutional neural network fostered Breast Cancer Classification using Mammogram Images (BC-APPDRC-DCNN-KHO). Here, the input images are taken from Real time and MAMMOSET datasets. These images are pre-processed using Altered Phase Preserving Dynamic Range Compression (APPDRC) technique. This APPDRC is applied for preserving local features, compressing dynamic range of images, and enhancing the speckle noise filtering, these are all necessary for better boundary detection. Then, the Pre-processed images are classified using Deep Convolutional neural network (DCNN). The DCNN weight parameters are optimized based on Krill Herd Optimization algorithm. The Proposed BCC-DCNN-KHO-MI method classifies the input breast cancer imageries into 3 categories: benign, malignant, and normal. The pro-

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Section: Resultsmentioning

confidence: 99%

Section: Literature Surveymentioning

confidence: 99%

Section: Research Gapmentioning

confidence: 99%

Section: Research Gapmentioning

confidence: 99%

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Krill herd optimization algorithm with deep convolutional neural network fostered breast cancer classification using mammogram images

Bai

Krish

2023

Concurrency and Computation

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“…To the best of our knowledge, only a handful of studies have focused on using recent algorithms such as CatB, XGB, and the ensemble stacking technique for fuel type classification. In addition, feature selection and automated hyperparameter tuning have been proven to increase the accuracy of classification models in different studies (e.g., [44][45][46]). The remainder of this paper is organised as follows: the dataset, main ML methods used, and hyperparameter optimisation approach are presented in Section 2; the experimental results are presented in Section 3 and discussed in Section 4; and our conclusions are provided in Section 5.…”

Section: Introductionmentioning

confidence: 99%

Vegetation Cover Type Classification Using Cartographic Data for Prediction of Wildfire Behaviour

Sadrabadi

Innocente

2023

Fire

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Predicting the behaviour of wildfires can help save lives and reduce health, socioeconomic, and environmental impacts. Because wildfire behaviour is highly dependent on fuel type and distribution, their accurate estimation is paramount for accurate prediction of the fire propagation dynamics. This paper studies the effect of combining automated hyperparameter tuning with Bayesian optimisation and recursive feature elimination on the accuracy of three boosting (AdaB, XGB, CatB), two bagging (Random Forest, Extremely Randomised Trees), and three stacking ensemble models with respect to their ability to estimate the vegetation cover type from cartographic data. The models are trained on the University of California Irvine (UCI) cover type dataset using five-fold cross-validation. Feature importance scores are calculated and used in recursive feature elimination analysis to study the sensitivity of model accuracy to the different feature combinations. Our results indicate that the implemented fine-tuning procedure significantly affects the accuracy of all models investigated, with XGB achieving an overall accuracy of 97.1% slightly outperforming the others.

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Breast lesion classification using features fusion and selection of ensemble ResNet method

Kilicarslan¹,

Koç

Özyurt

et al. 2023

Int J Imaging Syst Tech

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Medical Imaging with Deep Learning has recently become the most prominent topic in the scientific world. Significant results have been obtained in the classification of medical images using deep learning methods, and there has been an increase in studies on malignant types. The main reason for choosing breast cancer is that breast cancer is one of the critical malignant types that increase the death rate in women. In this study, 1236 ultrasound images were collected from Elazig Fethi Sekin City Hospital, and three different ResNet CNN architectures were used for feature extraction. Data were trained with an SVM classifier. In addition, the three ResNet architectures were combined, and novel fused ResNet architecture was used in this study. In addition, these features were used with three different feature selection techniques, MR‐MR, NCA, and Relieff. These results are 89.3% obtained from ALL‐ResNet architecture and the feature selected with NCA in normal and lesion classification. Normal, malignant, and benign classification best accuracy is 84.9% with ALL‐ResNet NCA. Experimental studies show that MR‐MR, NCA, and Relieff feature selection algorithms reduce features and give more results that are successful. This indicates that the proposed method is more successful than classical deep learning methods.

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Combining the advantages of radiomic features based feature extraction and hyper parameters tuned RERNN using LOA for breast cancer classification

Cited by 24 publications

References 37 publications

Krill herd optimization algorithm with deep convolutional neural network fostered breast cancer classification using mammogram images

Krill herd optimization algorithm with deep convolutional neural network fostered breast cancer classification using mammogram images

Vegetation Cover Type Classification Using Cartographic Data for Prediction of Wildfire Behaviour

Breast lesion classification using features fusion and selection of ensemble ResNet method

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