Addressing architectural distortion in mammogram using AlexNet and support vector machine

Vedalankar, Aditi V.; Gupta, Shankar S.; Manthalkar, Ramchandra

doi:10.1016/j.imu.2021.100551

Cited by 18 publications

(10 citation statements)

References 24 publications

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“…Each image in [ 10 ] remarkably turns into 546 new samples, and the post-augmentation performances improved by almost 25%. Fourteen is the dataset size growing factor in [ 89 ], moving from 215 to 3006 ROIs (Regions of Interest). Muduli et al [ 35 ] were able to extend MIAS, DDSM and INbreast, respectively, by 10, 19.2 and 5.4 times their original sizes.…”

Section: Discussionmentioning

confidence: 99%

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Image Augmentation Techniques for Mammogram Analysis

et al. 2022

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Research in the medical imaging field using deep learning approaches has become progressively contingent. Scientific findings reveal that supervised deep learning methods’ performance heavily depends on training set size, which expert radiologists must manually annotate. The latter is quite a tiring and time-consuming task. Therefore, most of the freely accessible biomedical image datasets are small-sized. Furthermore, it is challenging to have big-sized medical image datasets due to privacy and legal issues. Consequently, not a small number of supervised deep learning models are prone to overfitting and cannot produce generalized output. One of the most popular methods to mitigate the issue above goes under the name of data augmentation. This technique helps increase training set size by utilizing various transformations and has been publicized to improve the model performance when tested on new data. This article surveyed different data augmentation techniques employed on mammogram images. The article aims to provide insights into basic and deep learning-based augmentation techniques.

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Section: Discussionmentioning

confidence: 99%

“…The method mixes inputs with additional random samples. Vedalankar et al [ 89 ] addressed the analysis of architectural distortion in mammograms with an integrated solution based on AlexNet and SVM. However, the solution heavily relies on TTA as the data augmentation technique for mammogram images.…”

Section: Test-time Augmentation (Tta)mentioning

confidence: 99%

Image Augmentation Techniques for Mammogram Analysis

et al. 2022

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“…Pre-trained networks are trained on an Imagenet database [ 40 ] consisting of 1000 image classes. Even though trained on non-biomedical images, pre-trained CNNs in combination with off-the-shelf classifiers were found successful in the detection of a wide range of diseases from X-ray images, including tuberculosis [ 41 ], breast cancer [ 42 ] and pneumonia [ 43 ]. The convolutional layers built on top of each other, learn more complex features for reliable classification tasks.…”

Section: Methodsmentioning

confidence: 99%

A computer-aided diagnosis system for the classification of COVID-19 and non-COVID-19 pneumonia on chest X-ray images by integrating CNN with sparse autoencoder and feed forward neural network

Gayathri¹,

Abraham²,

Sujarani³

et al. 2022

Computers in Biology and Medicine

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Several infectious diseases have affected the lives of many people and have caused great dilemmas all over the world. COVID-19 was declared a pandemic caused by a newly discovered virus named Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) by the World Health Organisation in 2019. RT-PCR is considered the golden standard for COVID-19 detection. Due to the limited RT-PCR resources, early diagnosis of the disease has become a challenge. Radiographic images such as Ultrasound, CT scans, X-rays can be used for the detection of the deathly disease. Developing deep learning models using radiographic images for detecting COVID-19 can assist in countering the outbreak of the virus. This paper presents a computer-aided detection model utilizing chest X-ray images for combating the pandemic. Several pre-trained networks and their combinations have been used for developing the model. The method uses features extracted from pre-trained networks along with Sparse autoencoder for dimensionality reduction and a Feed Forward Neural Network (FFNN) for the detection of COVID-19. Two publicly available chest X-ray image datasets, consisting of 504 COVID-19 images and 542 non-COVID-19 images, have been combined to train the model. The method was able to achieve an accuracy of 0.957 8 and an AUC of 0.982 1, using the combination of InceptionResnetV2 and Xception. Experiments have proved that the accuracy of the model improves with the usage of sparse autoencoder as the dimensionality reduction technique.

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“…This method also reduces overfitting and is suiTable for a large number of features. Current deep learning methods are often using convolution layers to extract features and using fully connected layers for classification such as LeNet-5 [18], AlexNet [19], VGG-16 [11], GoogleNet [12], ResNet-50 [13], and DenseNet [14]. Among these deep learning methods, VGG-16 and ResNet-50 have a remarkable change when compared with other methods.…”

Section: Introductionmentioning

confidence: 99%

Comparative study on the performance of face recognition algorithms

Nguyen,

Chu

2023

Eureka: PE

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Facial and object recognition are more and more applied in our life. Therefore, this field has become important to both academicians and practitioners. Face recognition systems are complex systems using features of the face to recognize. Current face recognition systems may be used to increase work efficiency in various methods, including smart homes, online banking, traffic, sports, robots, and others. With various applications like this, the number of facial recognition methods has been increasing in recent years. However, the performance of face recognition systems can be significantly affected by various factors such as lighting conditions, and different types of masks (sunglasses, scarves, hats, etc.). In this paper, a detailed comparison between face recognition techniques is exposed by listing the structure of each model, the advantages and disadvantages as well as performing experiments to demonstrate the robustness, accuracy, and complexity of each algorithm. To be detailed, let’s give a performance comparison of three methods for measuring the efficacy of face recognition systems including a support vector machine (SVM), a visual geometry group with 16 layers (VGG-16), and a residual network with 50 layers (ResNet-50) in real-life settings. The efficiency of algorithms is evaluated in various environments such as normal light indoors, backlit indoors, low light indoors, natural light outdoors, and backlit outdoors. In addition, this paper also evaluates faces with hats and glasses to examine the accuracy of the methods. The experimental results indicate that the ResNet-50 has the highest accuracy to identify faces. The time to recognize is ranging from 1.1s to 1.2s in the normal environment

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Addressing architectural distortion in mammogram using AlexNet and support vector machine

Cited by 18 publications

References 24 publications

Image Augmentation Techniques for Mammogram Analysis

Image Augmentation Techniques for Mammogram Analysis

A computer-aided diagnosis system for the classification of COVID-19 and non-COVID-19 pneumonia on chest X-ray images by integrating CNN with sparse autoencoder and feed forward neural network

Comparative study on the performance of face recognition algorithms

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