Breast Cancer Detection and Classification using Deep Learning Xception Algorithm

Abunasser, Basem S.; AL-Hiealy, Mohammed Rasheed J.; Zaqout, Ihab; Abu-Naser, Samy S.

doi:10.14569/ijacsa.2022.0130729

Cited by 43 publications

(19 citation statements)

References 20 publications

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“…For future work, they seek to improve the accuracy of the models with new tuning parameters and optimizers. In [27] they presented a model based on Xception for the detection of breast cancer in real-time. The InceptionV3 model, which is the best performing model in this work, can be extended to classify other diseases as [27] has done with good results.…”

Section: Discussionmentioning

confidence: 99%

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Convolutional Neural Networks with Transfer Learning for Pneumonia Detection

Iparraguirre-Villanueva¹,

Guevara-Ponce²,

Paredes³

et al. 2022

IJACSA

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Pneumonia is a type of acute respiratory infection caused by microbes, and viruses that affect the lungs. Pneumonia is the leading cause of infant mortality in the world, accounting for 81% of deaths in children under five years of age. There are approximately 1.2 million cases of pneumonia in children under five years of age and 180 000 died in 2016. Early detection of pneumonia can help reduce mortality rates. Therefore, this paper presents four convolutional neural network (CNN) models to detect pneumonia from chest X-ray images. CNNs were trained to classify X-ray images into two types: normal and pneumonia, using several convolutional layers. The four models used in this work are pre-trained: VGG16, VGG19, ResNet50, and InceptionV3. The measures that were used for the evaluation of the results are Accuracy, recall, and F1-Score. The models were trained and validated with the dataset. The results showed that the Inceptionv3 model achieved the best performance with 72.9% accuracy, recall 93.7%, and F1-Score 82%. This indicates that CNN models are suitable for detecting pneumonia with high accuracy.

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Section: Discussionmentioning

confidence: 99%

“…In [27] they presented a model based on Xception for the detection of breast cancer in real-time. The InceptionV3 model, which is the best performing model in this work, can be extended to classify other diseases as [27] has done with good results. The performance of the models can be improved with a larger amount of data.…”

Section: Discussionmentioning

confidence: 99%

Convolutional Neural Networks with Transfer Learning for Pneumonia Detection

Iparraguirre-Villanueva¹,

Guevara-Ponce²,

Paredes³

et al. 2022

IJACSA

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“…Sivapriya, Kumar, et al, 2019 [24], compared SVM, logistic regression, naive Bayes, and random forest to determine their parallels and distinctions. Wisconsin's breast cancer dataset is used for comparative purposes (Abunasser, AL-Hiealy et al, 2022) [25]. The results of the evaluations showed that the random forest algorithm achieved the highest level of accuracy (99.76%) with the least amount of error.…”

Section: Literature Reviewmentioning

confidence: 99%

Breast Cancer Detection and Prevention Using Machine Learning

Khalid,

Mehmood,

Alabrah

et al. 2023

Diagnostics

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Breast cancer is a common cause of female mortality in developing countries. Early detection and treatment are crucial for successful outcomes. Breast cancer develops from breast cells and is considered a leading cause of death in women. This disease is classified into two subtypes: invasive ductal carcinoma (IDC) and ductal carcinoma in situ (DCIS). The advancements in artificial intelligence (AI) and machine learning (ML) techniques have made it possible to develop more accurate and reliable models for diagnosing and treating this disease. From the literature, it is evident that the incorporation of MRI and convolutional neural networks (CNNs) is helpful in breast cancer detection and prevention. In addition, the detection strategies have shown promise in identifying cancerous cells. The CNN Improvements for Breast Cancer Classification (CNNI-BCC) model helps doctors spot breast cancer using a trained deep learning neural network system to categorize breast cancer subtypes. However, they require significant computing power for imaging methods and preprocessing. Therefore, in this research, we proposed an efficient deep learning model that is capable of recognizing breast cancer in computerized mammograms of varying densities. Our research relied on three distinct modules for feature selection: the removal of low-variance features, univariate feature selection, and recursive feature elimination. The craniocaudally and medial-lateral views of mammograms are incorporated. We tested it with a large dataset of 3002 merged pictures gathered from 1501 individuals who had digital mammography performed between February 2007 and May 2015. In this paper, we applied six different categorization models for the diagnosis of breast cancer, including the random forest (RF), decision tree (DT), k-nearest neighbors (KNN), logistic regression (LR), support vector classifier (SVC), and linear support vector classifier (linear SVC). The simulation results prove that our proposed model is highly efficient, as it requires less computational power and is highly accurate.

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“…Ref. [ 42 ] conducted another study on BC using the Xception method, a form of the DL model. The study found that the Xception algorithm accurately classified breast cancer histopathology images.…”

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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Breast Cancer Detection and Classification using Deep Learning Xception Algorithm

Cited by 43 publications

References 20 publications

Convolutional Neural Networks with Transfer Learning for Pneumonia Detection

Convolutional Neural Networks with Transfer Learning for Pneumonia Detection

Breast Cancer Detection and Prevention Using Machine Learning

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

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