Digital Analysis in Breast Imaging

G, Negrão de Figueiredo; Ingrisch, Michael; Fallenberg, Eva Maria

doi:10.1159/000501099

Cited by 4 publications

(2 citation statements)

References 87 publications

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“…The preliminary results provide scopes for the further exploration of CNN. In an analysis conducted by [ 39 ], it was reported that there is an urgent need to improve imaging analysis and explore detection techniques using AI for breast cancer detection. Breast cancer is determined by several abnormalities and thus, this work contributes in detecting and classifying these abnormalities.…”

Section: Results and Performance Evaluationmentioning

confidence: 99%

Multi- class classification of breast cancer abnormalities using Deep Convolutional Neural Network (CNN)

et al. 2021

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The real cause of breast cancer is very challenging to determine and therefore early detection of the disease is necessary for reducing the death rate due to risks of breast cancer. Early detection of cancer boosts increasing the survival chance up to 8%. Primarily, breast images emanating from mammograms, X-Rays or MRI are analyzed by radiologists to detect abnormalities. However, even experienced radiologists face problems in identifying features like micro-calcifications, lumps and masses, leading to high false positive and high false negative. Recent advancement in image processing and deep learning create some hopes in devising more enhanced applications that can be used for the early detection of breast cancer. In this work, we have developed a Deep Convolutional Neural Network (CNN) to segment and classify the various types of breast abnormalities, such as calcifications, masses, asymmetry and carcinomas, unlike existing research work, which mainly classified the cancer into benign and malignant, leading to improved disease management. Firstly, a transfer learning was carried out on our dataset using the pre-trained model ResNet50. Along similar lines, we have developed an enhanced deep learning model, in which learning rate is considered as one of the most important attributes while training the neural network. The learning rate is set adaptively in our proposed model based on changes in error curves during the learning process involved. The proposed deep learning model has achieved a performance of 88% in the classification of these four types of breast cancer abnormalities such as, masses, calcifications, carcinomas and asymmetry mammograms.

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Section: Results and Performance Evaluationmentioning

confidence: 99%

Multi- class classification of breast cancer abnormalities using Deep Convolutional Neural Network (CNN)

et al. 2021

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“…Research has shown that the application of artificial intelligence and machine learning during diagnosis has helped further improve cancer detection and staging [11,12]. Computer-aided diagnosis has helped with automating labor-intensive steps and reducing reader bias [13][14][15]. Several studies have shown that accurate breast cancer predictions may depend on the right combination of feature selection and/or ML techniques [16,17].…”

Section: Introductionmentioning

confidence: 99%

A Clinical Decision Support Tool to Detect Invasive Ductal Carcinoma in Histopathological Images Using Support Vector Machines, Naïve-Bayes, and K-Nearest Neighbor Classifiers

Lopez

Magboo

2020

Machine Learning and Artificial Intelligence

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This study aims to describe a model that will apply image processing and traditional machine learning techniques specifically Support Vector Machines, Naïve-Bayes, and k-Nearest Neighbors to identify whether or not a given breast histopathological image has Invasive Ductal Carcinoma (IDC). The dataset consisted of 54,811 breast cancer image patches of size 50px x 50px, consisting of 39,148 IDC negative and 15,663 IDC positive. Feature extraction was accomplished using Oriented FAST and Rotated BRIEF (ORB) descriptors. Feature scaling was performed using Min-Max Normalization while K-Means Clustering on the ORB descriptors was used to generate the visual codebook. Automatic hyperparameter tuning using Grid Search Cross Validation was implemented although it can also accept user supplied hyperparameter values for SVM, Naïve Bayes, and K-NN models should the user want to do experimentation. Aside from computing for accuracy, the AUPRC and MCC metrics were used to address the dataset imbalance. The results showed that SVM has the best overall performance, obtaining accuracy = 0.7490, AUPRC = 0.5536, and MCC = 0.2924.

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Revolutionizing Breast Cancer Care: Cutting-Edge Breakthroughs and Future Frontiers in Precision Medicine

Qureshi,

Khan,

Imran

et al. 2024

Interdisciplinary Cancer Research

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Digital Analysis in Breast Imaging

Cited by 4 publications

References 87 publications

Multi- class classification of breast cancer abnormalities using Deep Convolutional Neural Network (CNN)

Multi- class classification of breast cancer abnormalities using Deep Convolutional Neural Network (CNN)

A Clinical Decision Support Tool to Detect Invasive Ductal Carcinoma in Histopathological Images Using Support Vector Machines, Naïve-Bayes, and K-Nearest Neighbor Classifiers

Revolutionizing Breast Cancer Care: Cutting-Edge Breakthroughs and Future Frontiers in Precision Medicine

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