Three-Class Mammogram Classification Based on Descriptive CNN Features

Jadoon, Muhammad Mohsin; Zhang, Qianni; Haq, İhsan Ul; Butt, Sharjeel; Jadoon, Adeel

doi:10.1155/2017/3640901

Cited by 132 publications

(83 citation statements)

References 38 publications

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“…Ekualisasi histogram merupakan proses pemerataan histogram, dimana distribusi nilai derajat keabuan pada suatu citra dibuat rata [5]. Untuk dapat melakukan ekualisasi histogram diperlukan suatu fungsi ditribusi kumulatif.…”

Section: Ekualisasi Histogramunclassified

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Perbaikan Kontras Citra Mammogram Pada Klasifikasi Kanker Payudara Berdasarkan Fitur Gray-Level Co-Occurrence Matrix

Liantoni

Santoso

2020

SINTECH Journal

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In this era to recognize breast tumors can be based on mammogram images. This method will expedite the process of recognition and classification of breast cancer. This research was conducted classification techniques of breast cancer using mammogram images. The proposed model targets classification studies for cases of malignant, and benign cancer. The research consisted of five main stages, preprocessing, histogram equalization, convolution, feature extraction, and classification. For preprocessing cropping the image using region of interest (ROI), for convolution, median filter and histogram equalization are used to improve image quality. Feature extraction using Gray-Level Co-Occurrence Matrix (GLCM) with 5 features, entropy, correlation, contrast, homogeneity, and variance. The final step is the classification using Radial Basis Function Neural Network (RBFNN) and Support Vector Machine (SVM). Based on the hypotheses that have been tested and discussed, the accuracy for RBFNN is 86.27%, while the accuracy for SVM is 84.31%. This shows that the RBFNN method is better than SVM in distinguishing types of breast cancer. These results prove the process of improving image construction using histogram equalization and the median filter is useful in the classification process.

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Section: Ekualisasi Histogramunclassified

“…Untuk preprosesing dilakukan cropping citra menggunakan region of interest (ROI), untuk konvolusi digunakan median filter dan ekualisasi histogram yang bertujuan untuk peningkatan kualitas citra. Ekstraksi fitur menggunakan Gray-Level Co-Occurrence Matrix (GLCM) dengan 5…”

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Perbaikan Kontras Citra Mammogram Pada Klasifikasi Kanker Payudara Berdasarkan Fitur Gray-Level Co-Occurrence Matrix

Liantoni

Santoso

2020

SINTECH Journal

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“…Jadoon et al [17] proposed a three-class (normal, malignant, and benign) mammogram classification using the CNN. This work presented two algorithms: the first based on Discrete Wavelet Transform (CNN-DW); the second bases on Curvelet Transform (CNN-CT).…”

Section: Literature Reviewmentioning

confidence: 99%

A Deep Learning Approach for Breast Cancer Mass Detection

Fathy¹,

Ghoneim²

2019

ijacsa

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Breast cancer is the most widespread type of cancer among women. The diagnosis of breast cancer in its early stages is still a significant problem worldwide. The accurate classification and localization of breast mass help in the early detection of the disease, so in the last few years, a variety of CAD systems are developed to enhance breast cancer classification and localization accuracy, but most of them are fully based on handcrafted feature extraction techniques, which affect its efficiency. Currently, deep learning approaches are able to automatically learn a set of high-level features and consequently, they are achieving remarkable results in object classification and detection tasks. In this paper, the pre-trained ResNet-50 architecture and the Class Activation Map (CAM) technique are employed in breast cancer classification and localization respectively. CAM technique exploits the Convolutional Neural Network (CNN) classifiers with Global Average Pooling (GAP) layer for object localization without any supervised information about its location. According to the experimental results, the proposed approach achieved 96% Area under the Receiver Operating Characteristics (ROC) curve in the classification with 99.8% sensitivity and 82.1% specificity. Furthermore, it is able to localize 93.67% of the masses at an average of 0.122 false positives per image on the Digital Database for Screening Mammography (DDSM) data-set. It is worth noting that the pretrained CNN is able automatically to learn the most discriminative features in the mammogram, and then fulfills superior results in breast cancer classification (normal or mass). Additionally, CAM exhibits the concrete relation between the mass located in the mammogram and the discriminative features learned by the CNN.

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“…is will minimize the variance between the training and validation sets and any future testing sets. Data augmentation has been used in many studies along with DL and CNN such as [192][193][194][195][196].…”

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confidence: 99%

Methods Used in Computer-Aided Diagnosis for Breast Cancer Detection Using Mammograms: A Review

Ramadan

2020

Journal of Healthcare Engineering

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According to the American Cancer Society’s forecasts for 2019, there will be about 268,600 new cases in the United States with invasive breast cancer in women, about 62,930 new noninvasive cases, and about 41,760 death cases from breast cancer. As a result, there is a high demand for breast imaging specialists as indicated in a recent report for the Institute of Medicine and National Research Council. One way to meet this demand is through developing Computer-Aided Diagnosis (CAD) systems for breast cancer detection and diagnosis using mammograms. This study aims to review recent advancements and developments in CAD systems for breast cancer detection and diagnosis using mammograms and to give an overview of the methods used in its steps starting from preprocessing and enhancement step and ending in classification step. The current level of performance for the CAD systems is encouraging but not enough to make CAD systems standalone detection and diagnose clinical systems. Unless the performance of CAD systems enhanced dramatically from its current level by enhancing the existing methods, exploiting new promising methods in pattern recognition like data augmentation in deep learning and exploiting the advances in computational power of computers, CAD systems will continue to be a second opinion clinical procedure.

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Three-Class Mammogram Classification Based on Descriptive CNN Features

Cited by 132 publications

References 38 publications

Perbaikan Kontras Citra Mammogram Pada Klasifikasi Kanker Payudara Berdasarkan Fitur Gray-Level Co-Occurrence Matrix

Perbaikan Kontras Citra Mammogram Pada Klasifikasi Kanker Payudara Berdasarkan Fitur Gray-Level Co-Occurrence Matrix

A Deep Learning Approach for Breast Cancer Mass Detection

Methods Used in Computer-Aided Diagnosis for Breast Cancer Detection Using Mammograms: A Review

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