Hybridized Deep Convolutional Neural Network and Fuzzy Support Vector Machines for Breast Cancer Detection

Oyetade, Idowu Sunday; Ayeni, Joshua Ojo; Ogunde, Adewale Opeoluwa; Oguntunde, Bosede Oyenike; Olowookere, Toluwase Ayobami

doi:10.1007/s42979-021-00882-4

Cited by 12 publications

(12 citation statements)

References 20 publications

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“…[3] and Muduli et al. [21] used the CNN architecture built from scratch and Customised CNN with less tuneable parameters and attained the accuracy of 91.2% and 90.68%, respectively. Recently, Oyetade et al.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

“…CNN detects features of an image like edges, corners etc., thus eliminating the feature extraction process by absorbing it in their architecture. A CNN has various layers, including input layer, processing layer consists of multiple convolutional, ReLU, and pooling layers, which are used to extract numerous features of an image followed by the fully connected layer that uses the features produced by previous layers in order to classify the image [21]. In addition, there are components, such as neurons, weights, bias factor, and activation functions (binary, linear, and non-linear).…”

Section: Deep Feature Extractionmentioning

confidence: 99%

“…Vivek et al [24] applied cGAN (CGAN) and CNN for BrC classification and attained an accuracy of 80%. Enas et al [3] and Muduli et al [21] used the CNN architecture built from scratch and Customised CNN with less tuneable parameters and attained the accuracy of 91.2% and 90.68%, respectively. Recently, Oyetade et al [28] employed AlexNet for feature extraction and deep CNN with fuzzy SVM for final classification to attain an accuracy of 91.65%.…”

Section: Standard Error Mean (Sem)-based Analysismentioning

confidence: 99%

“…Oyetade et al. [21] presented a CAD system for BrC based on a hybrid of deep CNN with fuzzy SVM. They can efficiently diagnose and classify in both binary and multi‐class scenarios.…”

Section: Related Workmentioning

confidence: 99%

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B²C³NetF²: Breast cancer classification using an end‐to‐end deep learning feature fusion and satin bowerbird optimization controlled Newton Raphson feature selection

Fatima

Khan

Shaheen

et al. 2023

CAAI Trans on Intel Tech

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Currently, the improvement in AI is mainly related to deep learning techniques that are employed for the classification, identification, and quantification of patterns in clinical images. The deep learning models show more remarkable performance than the traditional methods for medical image processing tasks, such as skin cancer, colorectal cancer, brain tumour, cardiac disease, Breast cancer (BrC), and a few more. The manual diagnosis of medical issues always requires an expert and is also expensive. Therefore, developing some computer diagnosis techniques based on deep learning is essential. Breast cancer is the most frequently diagnosed cancer in females with a rapidly growing percentage. It is estimated that patients with BrC will rise to 70% in the next 20 years. If diagnosed at a later stage, the survival rate of patients with BrC is shallow. Hence, early detection is essential, increasing the survival rate to 50%. A new framework for BrC classification is presented that utilises deep learning and feature optimization. The significant steps of the presented framework include (i) hybrid contrast enhancement of acquired images, (ii) data augmentation to facilitate better learning of the Convolutional Neural Network (CNN) model, (iii) a pre‐trained ResNet‐101 model is utilised and modified according to selected dataset classes, (iv) deep transfer learning based model training for feature extraction, (v) the fusion of features using the proposed highly corrected function‐controlled canonical correlation analysis approach, and (vi) optimal feature selection using the modified Satin Bowerbird Optimization controlled Newton Raphson algorithm that finally classified using 10 machine learning classifiers. The experiments of the proposed framework have been carried out using the most critical and publicly available dataset, such as CBIS‐DDSM, and obtained the best accuracy of 94.5% along with improved computation time. The comparison depicts that the presented method surpasses the current state‐of‐the‐art approaches.

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Section: Experimental Results and Analysismentioning

confidence: 99%

Section: Deep Feature Extractionmentioning

confidence: 99%

Section: Standard Error Mean (Sem)-based Analysismentioning

confidence: 99%

“…Oyetade et al. [21] presented a CAD system for BrC based on a hybrid of deep CNN with fuzzy SVM. They can efficiently diagnose and classify in both binary and multi‐class scenarios.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

B²C³NetF²: Breast cancer classification using an end‐to‐end deep learning feature fusion and satin bowerbird optimization controlled Newton Raphson feature selection

Fatima

Khan

Shaheen

et al. 2023

CAAI Trans on Intel Tech

View full text Add to dashboard Cite

show abstract

“…They found out that rank-based stochastic process is the best-suited algorithm, obtaining an accuracy, sensibility, and specificity of 94.0%, 93.4%, and 94.6%, respectively, for classifying lesions for normal or abnormal using mammograms. Similar approaches have been proposed [ 152 , 153 , 154 , 155 ]. Table 2 presents a summary of the classifiers above discussed.…”

Section: Image Processing and Classification Strategiesmentioning

confidence: 99%

Diagnostic Strategies for Breast Cancer Detection: From Image Generation to Classification Strategies Using Artificial Intelligence Algorithms

Basurto-Hurtado

Cruz-Albarran

Toledano‐Ayala

et al. 2022

Cancers

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Breast cancer is one the main death causes for women worldwide, as 16% of the diagnosed malignant lesions worldwide are its consequence. In this sense, it is of paramount importance to diagnose these lesions in the earliest stage possible, in order to have the highest chances of survival. While there are several works that present selected topics in this area, none of them present a complete panorama, that is, from the image generation to its interpretation. This work presents a comprehensive state-of-the-art review of the image generation and processing techniques to detect Breast Cancer, where potential candidates for the image generation and processing are presented and discussed. Novel methodologies should consider the adroit integration of artificial intelligence-concepts and the categorical data to generate modern alternatives that can have the accuracy, precision and reliability expected to mitigate the misclassifications.

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Deep learning network selection and optimized information fusion for enhanced COVID‐19 detection

Ali,

Zafar,

Tanveer

et al. 2023

Int J Imaging Syst Tech

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This study proposes a wrapper‐based technique to improve the classification performance of chest infection (including COVID‐19) detection using X‐rays. Deep features were extracted using pretrained deep learning models. Ten optimization techniques, including poor and rich optimization, path finder algorithm, Henry gas solubility optimization, Harris hawks optimization, atom search optimization, manta‐ray foraging optimization, equilibrium optimizer, slime mold algorithm, generalized normal distribution optimization, and marine predator algorithm, were used to determine the optimal features using a support vector machine. Moreover, a network selection technique was used to select the deep learning models. An online chest infection detection X‐ray scan dataset was used to validate the proposed approach. The results suggest that the proposed wrapper‐based automatic deep learning network selection and feature optimization framework has a high classification rate of 97.7%. The comparative analysis further validates the credibility of the framework in COVID‐19 and other chest infection classifications, suggesting that the proposed approach can help doctors in clinical practice.

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Hybridized Deep Convolutional Neural Network and Fuzzy Support Vector Machines for Breast Cancer Detection

Cited by 12 publications

References 20 publications

B²C³NetF²: Breast cancer classification using an end‐to‐end deep learning feature fusion and satin bowerbird optimization controlled Newton Raphson feature selection

B²C³NetF²: Breast cancer classification using an end‐to‐end deep learning feature fusion and satin bowerbird optimization controlled Newton Raphson feature selection

Diagnostic Strategies for Breast Cancer Detection: From Image Generation to Classification Strategies Using Artificial Intelligence Algorithms

Deep learning network selection and optimized information fusion for enhanced COVID‐19 detection

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Hybridized Deep Convolutional Neural Network and Fuzzy Support Vector Machines for Breast Cancer Detection

Cited by 12 publications

References 20 publications

B2C3NetF2: Breast cancer classification using an end‐to‐end deep learning feature fusion and satin bowerbird optimization controlled Newton Raphson feature selection

B2C3NetF2: Breast cancer classification using an end‐to‐end deep learning feature fusion and satin bowerbird optimization controlled Newton Raphson feature selection

Diagnostic Strategies for Breast Cancer Detection: From Image Generation to Classification Strategies Using Artificial Intelligence Algorithms

Deep learning network selection and optimized information fusion for enhanced COVID‐19 detection

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Product

Resources

About

B²C³NetF²: Breast cancer classification using an end‐to‐end deep learning feature fusion and satin bowerbird optimization controlled Newton Raphson feature selection

B²C³NetF²: Breast cancer classification using an end‐to‐end deep learning feature fusion and satin bowerbird optimization controlled Newton Raphson feature selection