2018
DOI: 10.3390/info9010019
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Histopathological Breast-Image Classification Using Local and Frequency Domains by Convolutional Neural Network

Abstract: Identification of the malignancy of tissues from Histopathological images has always been an issue of concern to doctors and radiologists. This task is time-consuming, tedious and moreover very challenging. Success in finding malignancy from Histopathological images primarily depends on long-term experience, though sometimes experts disagree on their decisions. However, Computer Aided Diagnosis (CAD) techniques help the radiologist to give a second opinion that can increase the reliability of the radiologist's… Show more

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Cited by 98 publications
(31 citation statements)
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References 46 publications
(51 reference statements)
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“…Lastly, an accuracy of 91% is achieved. In addition, in the work of [68], five DCNN models are built up, considering handcraft features and deep learning features jointly. In the experiment, the second model obtains the best performance of 92.19% accuracy.…”
Section: Related Work Of Breakhis In 2018mentioning
confidence: 99%
“…Lastly, an accuracy of 91% is achieved. In addition, in the work of [68], five DCNN models are built up, considering handcraft features and deep learning features jointly. In the experiment, the second model obtains the best performance of 92.19% accuracy.…”
Section: Related Work Of Breakhis In 2018mentioning
confidence: 99%
“…They were able to improve their results by using the AlexNet network for transfer learning. The study [10] evaluated CNN with multiple handcrafted features and compared the results with those provided with raw images. They achieved their best results by using residual blocks inspired by ResNet.…”
Section: Binary Classificationmentioning
confidence: 99%
“…Authors in [26] proposed a CNN model for breast cancer classification considering the local and frequency domain information using Histopathological images. The objective is to utilize the important information of images that are carried by the local and frequency domain information which sometime shows better accuracy for the model.…”
Section: Introductionmentioning
confidence: 99%