Image based computer aided diagnosis system for cancer detection
H Lee, YPP Chen - Expert Systems with Applications, 2015 - Elsevier
Cancer is one of the major causes of non-accidental death in human. Early diagnosis of the
disease allows clinician to administer suitable treatment, and can improve the patient's …
disease allows clinician to administer suitable treatment, and can improve the patient's …
[HTML][HTML] Breast cancer histology images classification: Training from scratch or transfer learning?
R Mehra - Ict Express, 2018 - Elsevier
We demonstrated the ability of transfer learning in comparison with the fully-trained network
on the histopathological imaging modality by considering three pre-trained networks …
on the histopathological imaging modality by considering three pre-trained networks …
BreastNet18: a high accuracy fine-tuned VGG16 model evaluated using ablation study for diagnosing breast cancer from enhanced mammography images
Simple Summary Breast cancer diagnosis at an early stage using mammography is
important, as it assists clinical specialists in treatment planning to increase survival rates …
important, as it assists clinical specialists in treatment planning to increase survival rates …
A systematic review of machine and deep learning techniques for the identification and classification of breast cancer through medical image modalities
This paper examines and assesses state-of-the-art proposed machine and deep learning
techniques for breast cancer identification and classification based on breast screening …
techniques for breast cancer identification and classification based on breast screening …
A deep learning model using data augmentation for detection of architectural distortion in whole and patches of images
Breast cancer is now widely known to be the second most lethal disease among women.
Computer-aided detection (CAD) systems, deep learning (DL) in particular, have continued …
Computer-aided detection (CAD) systems, deep learning (DL) in particular, have continued …
Breast cancer screening using convolutional neural network and follow-up digital mammography
Y Zheng, C Yang, A Merkulov - Computational Imaging III, 2018 - spiedigitallibrary.org
We propose a computer-aided detection (CAD) method for breast cancer screening using
convolutional neural network (CNN) and follow-up scans. First, mammographic images are …
convolutional neural network (CNN) and follow-up scans. First, mammographic images are …
A comparison of different Gabor feature extraction approaches for mass classification in mammography
We investigate the performance of six different approaches for directional feature extraction
for mass classification problem in digital mammograms. These techniques use a bank of …
for mass classification problem in digital mammograms. These techniques use a bank of …
Optimized gabor feature extraction for mass classification using cuckoo search for big data e-healthcare
Widespread use of electronic health records is a major cause of a massive dataset that
ultimately results in Big Data. Computer-aided systems for healthcare can be an effective …
ultimately results in Big Data. Computer-aided systems for healthcare can be an effective …
Analysis of tissue abnormality and breast density in mammographic images using a uniform local directional pattern
This paper proposes a computer-aided diagnosis system to analyze breast tissues in
mammograms, which performs two main tasks: breast tissue classification within a region of …
mammograms, which performs two main tasks: breast tissue classification within a region of …
Breast cancer detection in mammography using spatial diversity, geostatistics, and concave geometry
Breast cancer is a global health problem which mainly affects the female population. It is
known that early detection increases the chances of effective treatment, improving the …
known that early detection increases the chances of effective treatment, improving the …