Breast cancer diagnosis based on hybrid rule-based feature selection with deep learning algorithm

Awotunde, Joseph Bamidele; Panigrahi, Ranjit; Khandelwal, Bidita; Garg, Amik; Bhoi, Akash Kumar

doi:10.1007/s42600-022-00255-7

Cited by 12 publications

(5 citation statements)

References 68 publications

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“…Deep CNNs utilizing transfer learning have shown promise in classifying medical images for accurate diagnosis [27,28,29]. Ongoing research continues to explore innovative deep learning architectures and techniques to advance breast cancer diagnosis and treatment [30][31][32][33][34][35][36][37][38][39][40][41][42][43].…”

Section: Related Workmentioning

confidence: 99%

Enhancing TNM Staging in Breast Cancer: A Hybrid Approach with CNN, Edge Detection, and Self-Organizing Maps for Improved Accuracy

Ajlouni,

Özyavaş,

Ajlouni

et al. 2024

Preprint

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Breast cancer remains a leading cause of mortality among women globally, underscoring the urgent need for improved diagnostic and staging techniques to enhance patient outcomes. This study aims to automate the TNM staging of breast cancer using a hybrid approach that integrates Convolutional Neural Networks (CNNs), edge detection methods, and Self-Organizing Maps (SOMs). Utilizing the Duke Breast Cancer MRI dataset, which provides detailed MRI scans crucial for accurate tumor characterization, the research addresses the limitations of traditional TNM staging, which often relies on manual interpretation by radiologists and can lead to inconsistencies and inaccuracies. Our approach combines CNNs with advanced edge detection algorithms and SOMs to automate and enhance the accuracy of breast cancer staging. The hybrid model effectively identifies and delineates tumor boundaries and critical anatomical features, offering a more reliable and objective evaluation. Notably, this method improves accuracy from 93% with conventional CNN models to 98%, representing a significant advancement in precision. This improvement not only provides more accurate diagnoses but also enables more personalized and effective treatment plans. For patients, this enhanced accuracy translates to better prognostic assessments and tailored treatments, potentially leading to improved outcomes and reduced likelihood of overtreatment or under treatment. For medical staff, the improved accuracy reduces the likelihood of misdiagnoses and enhances workflow efficiency by minimizing manual interpretation, thus alleviating some of the burdens associated with cancer staging. The model's performance is optimized through various testing methods and statistical evaluations, validating its stability and reliability. The integration of edge detection and SOMs captures comprehensive information, prevents overfitting, and provides valuable insights into data clustering. This combined approach supports personalized medicine by ensuring treatments are customized to individual patient characteristics, ultimately contributing to better survival rates and quality of life for patients.

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Section: Related Workmentioning

confidence: 99%

Enhancing TNM Staging in Breast Cancer: A Hybrid Approach with CNN, Edge Detection, and Self-Organizing Maps for Improved Accuracy

Ajlouni,

Özyavaş,

Ajlouni

et al. 2024

Preprint

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“…Ref. [ 30 ] used deep learning and rule-based feature selection for breast cancer classification. Their approach used a deep CNN for feature extraction and a rule-based feature selection method for classification.…”

Section: Related Workmentioning

confidence: 99%

Breast Cancer Classification through Meta-Learning Ensemble Technique Using Convolution Neural Networks

Ali,

Saleem,

Elahi

et al. 2023

Diagnostics

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This study aims to develop an efficient and accurate breast cancer classification model using meta-learning approaches and multiple convolutional neural networks. This Breast Ultrasound Images (BUSI) dataset contains various types of breast lesions. The goal is to classify these lesions as benign or malignant, which is crucial for the early detection and treatment of breast cancer. The problem is that traditional machine learning and deep learning approaches often fail to accurately classify these images due to their complex and diverse nature. In this research, to address this problem, the proposed model used several advanced techniques, including meta-learning ensemble technique, transfer learning, and data augmentation. Meta-learning will optimize the model’s learning process, allowing it to adapt to new and unseen datasets quickly. Transfer learning will leverage the pre-trained models such as Inception, ResNet50, and DenseNet121 to enhance the model’s feature extraction ability. Data augmentation techniques will be applied to artificially generate new training images, increasing the size and diversity of the dataset. Meta ensemble learning techniques will combine the outputs of multiple CNNs, improving the model’s classification accuracy. The proposed work will be investigated by pre-processing the BUSI dataset first, then training and evaluating multiple CNNs using different architectures and pre-trained models. Then, a meta-learning algorithm will be applied to optimize the learning process, and ensemble learning will be used to combine the outputs of multiple CNN. Additionally, the evaluation results indicate that the model is highly effective with high accuracy. Finally, the proposed model’s performance will be compared with state-of-the-art approaches in other existing systems’ accuracy, precision, recall, and F1 score.

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“…Obayya et al [ 20 ] presented an optimized hyperparameter-based deep-learning framework for classifying breast cancer. After the training, the researcher extracted the deep features from the fully connected layer; however, based on the analysis, it was noticed that several features were redundant and affected the classification accuracy of breast cancer [ 21 ]. Recently, Atban et al [ 22 ] presented an optimized deep learning approach for improved breast cancer classification.…”

Section: Introductionmentioning

confidence: 99%

BC2NetRF: Breast Cancer Classification from Mammogram Images Using Enhanced Deep Learning Features and Equilibrium-Jaya Controlled Regula Falsi-Based Features Selection

et al. 2023

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One of the most frequent cancers in women is breast cancer, and in the year 2022, approximately 287,850 new cases have been diagnosed. From them, 43,250 women died from this cancer. An early diagnosis of this cancer can help to overcome the mortality rate. However, the manual diagnosis of this cancer using mammogram images is not an easy process and always requires an expert person. Several AI-based techniques have been suggested in the literature. However, still, they are facing several challenges, such as similarities between cancer and non-cancer regions, irrelevant feature extraction, and weak training models. In this work, we proposed a new automated computerized framework for breast cancer classification. The proposed framework improves the contrast using a novel enhancement technique called haze-reduced local-global. The enhanced images are later employed for the dataset augmentation. This step aimed at increasing the diversity of the dataset and improving the training capability of the selected deep learning model. After that, a pre-trained model named EfficientNet-b0 was employed and fine-tuned to add a few new layers. The fine-tuned model was trained separately on original and enhanced images using deep transfer learning concepts with static hyperparameters’ initialization. Deep features were extracted from the average pooling layer in the next step and fused using a new serial-based approach. The fused features were later optimized using a feature selection algorithm known as Equilibrium-Jaya controlled Regula Falsi. The Regula Falsi was employed as a termination function in this algorithm. The selected features were finally classified using several machine learning classifiers. The experimental process was conducted on two publicly available datasets—CBIS-DDSM and INbreast. For these datasets, the achieved average accuracy is 95.4% and 99.7%. A comparison with state-of-the-art (SOTA) technology shows that the obtained proposed framework improved the accuracy. Moreover, the confidence interval-based analysis shows consistent results of the proposed framework.

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Breast cancer diagnosis based on hybrid rule-based feature selection with deep learning algorithm

Cited by 12 publications

References 68 publications

Enhancing TNM Staging in Breast Cancer: A Hybrid Approach with CNN, Edge Detection, and Self-Organizing Maps for Improved Accuracy

Enhancing TNM Staging in Breast Cancer: A Hybrid Approach with CNN, Edge Detection, and Self-Organizing Maps for Improved Accuracy

Breast Cancer Classification through Meta-Learning Ensemble Technique Using Convolution Neural Networks

BC2NetRF: Breast Cancer Classification from Mammogram Images Using Enhanced Deep Learning Features and Equilibrium-Jaya Controlled Regula Falsi-Based Features Selection

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