Traditional machine learning algorithms for breast cancer image classification with optimized deep features

Atban, Furkan; Ekinci, Ekin; Garip, Zeynep

doi:10.1016/j.bspc.2022.104534

Cited by 33 publications

(16 citation statements)

References 54 publications

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“…Techniques such as Support Vector Machines (SVMs), Random Forests, and k-Nearest Neighbors (k-NN) were commonly employed for classification tasks. Although these methods enhanced efficiency, they faced difficulties in dealing with the complexity and variety present in breast cancer images, making accurate classification a persistent challenge ( 2 ).…”

Section: Literature Reviewmentioning

confidence: 99%

Enhancing accessibility for improved diagnosis with modified EfficientNetV2-S and cyclic learning rate strategy in women with disabilities and breast cancer

Al Moteri,

Mahesh,

Thakur

et al. 2024

Front. Med.

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Breast cancer, a prevalent cancer among women worldwide, necessitates precise and prompt detection for successful treatment. While conventional histopathological examination is the benchmark, it is a lengthy process and prone to variations among different observers. Employing machine learning to automate the diagnosis of breast cancer presents a viable option, striving to improve both precision and speed. Previous studies have primarily focused on applying various machine learning and deep learning models for the classification of breast cancer images. These methodologies leverage convolutional neural networks (CNNs) and other advanced algorithms to differentiate between benign and malignant tumors from histopathological images. Current models, despite their potential, encounter obstacles related to generalizability, computational performance, and managing datasets with imbalances. Additionally, a significant number of these models do not possess the requisite transparency and interpretability, which are vital for medical diagnostic purposes. To address these limitations, our study introduces an advanced machine learning model based on EfficientNetV2. This model incorporates state-of-the-art techniques in image processing and neural network architecture, aiming to improve accuracy, efficiency, and robustness in classification. We employed the EfficientNetV2 model, fine-tuned for the specific task of breast cancer image classification. Our model underwent rigorous training and validation using the BreakHis dataset, which includes diverse histopathological images. Advanced data preprocessing, augmentation techniques, and a cyclical learning rate strategy were implemented to enhance model performance. The introduced model exhibited remarkable efficacy, attaining an accuracy rate of 99.68%, balanced precision and recall as indicated by a significant F1 score, and a considerable Cohen’s Kappa value. These indicators highlight the model’s proficiency in correctly categorizing histopathological images, surpassing current techniques in reliability and effectiveness. The research emphasizes improved accessibility, catering to individuals with disabilities and the elderly. By enhancing visual representation and interpretability, the proposed approach aims to make strides in inclusive medical image interpretation, ensuring equitable access to diagnostic information.

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Section: Literature Reviewmentioning

confidence: 99%

Enhancing accessibility for improved diagnosis with modified EfficientNetV2-S and cyclic learning rate strategy in women with disabilities and breast cancer

Al Moteri,

Mahesh,

Thakur

et al. 2024

Front. Med.

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“…where h n+1 is the fittest best root value (feature) and h n+1 ∈ Old Population. Equations (18)(19)(20)(21)(22)(23)(24)(25)(26) are repeated until the best solution is not returned. The best selected features are finally classified using ML classifiers.…”

Section: Calculate New Changes In Bower's Positionmentioning

confidence: 99%

“…Early detection and classification of breast lesions from many MGS images utilising manual procedures have various challenges resulting in high FPR and FNR [26]. Therefore, in recent years, more current effort has been focussed on using ML and DL-based CAD systems in the early prognosis and classification of BrC [3,27].…”

Section: Related Workmentioning

confidence: 99%

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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“…The mean intensity value of the intensity-based set was found to be the second most significant feature. The ranking order was as follows: Feature number: 27,31,18,24,30,29,8,21,23,15,26,25,28,12,20,3,5,6,22,11,9,17,16,14,4,19,10,13,7, 32, 1, 2, 33. However, overall, directional features were found to be more significant than the geometrical and intensity-based features as 14 out of the 15 features in the ranking belonged to the directional features that were developed to represent the irregular border shape of the segmented cells in an image.…”

Section: Intensity-based Featuresmentioning

confidence: 99%

Deep Learning- and Expert Knowledge-Based Feature Extraction and Performance Evaluation in Breast Histopathology Images

Kode,

Barkana

2023

Cancers

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Cancer develops when a single or a group of cells grows and spreads uncontrollably. Histopathology images are used in cancer diagnosis since they show tissue and cell structures under a microscope. Knowledge-based and deep learning-based computer-aided detection is an ongoing research field in cancer diagnosis using histopathology images. Feature extraction is vital in both approaches since the feature set is fed to a classifier and determines the performance. This paper evaluates three feature extraction methods and their performance in breast cancer diagnosis. Features are extracted by (1) a Convolutional Neural Network, (2) a transfer learning architecture VGG16, and (3) a knowledge-based system. The feature sets are tested by seven classifiers, including Neural Network (64 units), Random Forest, Multilayer Perceptron, Decision Tree, Support Vector Machines, K-Nearest Neighbors, and Narrow Neural Network (10 units) on the BreakHis 400× image dataset. The CNN achieved up to 85% for the Neural Network and Random Forest, the VGG16 method achieved up to 86% for the Neural Network, and the knowledge-based features achieved up to 98% for Neural Network, Random Forest, Multilayer Perceptron classifiers.

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Traditional machine learning algorithms for breast cancer image classification with optimized deep features

Cited by 33 publications

References 54 publications

Enhancing accessibility for improved diagnosis with modified EfficientNetV2-S and cyclic learning rate strategy in women with disabilities and breast cancer

Enhancing accessibility for improved diagnosis with modified EfficientNetV2-S and cyclic learning rate strategy in women with disabilities and breast cancer

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

Deep Learning- and Expert Knowledge-Based Feature Extraction and Performance Evaluation in Breast Histopathology Images

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Traditional machine learning algorithms for breast cancer image classification with optimized deep features

Cited by 33 publications

References 54 publications

Enhancing accessibility for improved diagnosis with modified EfficientNetV2-S and cyclic learning rate strategy in women with disabilities and breast cancer

Enhancing accessibility for improved diagnosis with modified EfficientNetV2-S and cyclic learning rate strategy in women with disabilities and breast cancer

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

Deep Learning- and Expert Knowledge-Based Feature Extraction and Performance Evaluation in Breast Histopathology Images

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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