Breast Cancer Classification Using Transfer Learning

Seemendra, Animesh; Singh, Rahul; Singh, Sukhendra

doi:10.1007/978-981-15-7804-5_32

Cited by 10 publications

(2 citation statements)

References 18 publications

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“…The aim of the experiment was to improve the performance of single models. Seemendra et al (2021) analyzed various pre-trained CNN models with fine-tuning to detect and classify invasion ductal carcinoma. The models used were VGG16, VGG19, ResNet, DenseNet, MobileNet and EfficientNet.…”

Section: Challengesmentioning

confidence: 99%

Transfer learning for image classification using VGG19: Caltech-101 image data set

Bansal

Kumar

Sachdeva

et al. 2021

J Ambient Intell Human Comput

126

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Image classification is getting more attention in the area of computer vision. During the past few years, a lot of research has been done on image classification using classical machine learning and deep learning techniques. Presently, deep learningbased techniques have given stupendous results. The performance of a classification system depends on the quality of features extracted from an image. The better is the quality of extracted features, the more the accuracy will be. Although, numerous deep learning-based methods have shown enormous performance in image classification, still due to various challenges deep learning methods are not able to extract all the important information from the image. This results in a reduction in overall classification accuracy. The goal of the present research is to improve the image classification performance by combining the deep features extracted using popular deep convolutional neural network, VGG19, and various handcrafted feature extraction methods, i.e., SIFT, SURF, ORB, and Shi-Tomasi corner detector algorithm. Further, the extracted features from these methods are classified using various machine learning classification methods, i.e., Gaussian Naïve Bayes, Decision Tree, Random Forest, and eXtreme Gradient Boosting (XGBClassifier) classifier. The experiment is carried out on a benchmark dataset Caltech-101. The experimental results indicate that Random Forest using the combined features give 93.73% accuracy and outperforms other classifiers and methods proposed by other authors. The paper concludes that a single feature extractor whether shallow or deep is not enough to achieve satisfactory results. So, a combined approach using deep learning features and traditional handcrafted features is better for image classification.

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Section: Challengesmentioning

confidence: 99%

Transfer learning for image classification using VGG19: Caltech-101 image data set

Bansal

Kumar

Sachdeva

et al. 2021

J Ambient Intell Human Comput

126

View full text Add to dashboard Cite

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“…Transfer learning has been applied with pre-trained classifiers [12,13] with better performance. Although the attention mechanism [14][15][16][17] was primarily used for language processing-related tasks like image captioning [18][19][20][21][22], language translation [23].…”

Section: Introductionmentioning

confidence: 99%

Deep Attention Network for Pneumonia Detection Using Chest X-Ray Images

Singh¹,

Rawat²,

Gupta³

et al. 2023

Computers, Materials &Amp; Continua

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In computer vision, object recognition and image categorization have proven to be difficult challenges. They have, nevertheless, generated responses to a wide range of difficult issues from a variety of fields. Convolution Neural Networks (CNNs) have recently been identified as the most widely proposed deep learning (DL) algorithms in the literature. CNNs have unquestionably delivered cutting-edge achievements, particularly in the areas of image classification, speech recognition, and video processing. However, it has been noticed that the CNN-training assignment demands a large amount of data, which is in low supply, especially in the medical industry, and as a result, the training process takes longer. In this paper, we describe an attentionaware CNN architecture for classifying chest X-ray images to diagnose Pneumonia in order to address the aforementioned difficulties. Attention Modules provide attention-aware properties to the Attention Network. The attentionaware features of various modules alter as the layers become deeper. Using a bottom-up top-down feedforward structure, the feedforward and feedback attention processes are integrated into a single feedforward process inside each attention module. In the present work, a deep neural network (DNN) is combined with an attention mechanism to test the prediction of Pneumonia disease using chest X-ray pictures. To produce attention-aware features, the suggested network was built by merging channel and spatial attention modules in DNN architecture. With this network, we worked on a publicly available Kaggle chest X-ray dataset. Extensive testing was carried out to validate the suggested model. In the experimental results, we attained an accuracy of 95.47% and an F-score of 0.92, indicating that the suggested model outperformed against the baseline models.

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