3 research outputs found
Reduced Deep Convolutional Activation Features (R-DeCAF) in Histopathology Images to Improve the Classification Performance for Breast Cancer Diagnosis
Breast cancer is the second most common cancer among women worldwide.
Diagnosis of breast cancer by the pathologists is a time-consuming procedure
and subjective. Computer aided diagnosis frameworks are utilized to relieve
pathologist workload by classifying the data automatically, in which deep
convolutional neural networks (CNNs) are effective solutions. The features
extracted from activation layer of pre-trained CNNs are called deep
convolutional activation features (DeCAF). In this paper, we have analyzed that
all DeCAF features are not necessarily led to a higher accuracy in the
classification task and dimension reduction plays an important role. Therefore,
different dimension reduction methods are applied to achieve an effective
combination of features by capturing the essence of DeCAF features. To this
purpose, we have proposed reduced deep convolutional activation features
(R-DeCAF). In this framework, pre-trained CNNs such as AlexNet, VGG-16 and
VGG-19 are utilized in transfer learning mode as feature extractors. DeCAF
features are extracted from the first fully connected layer of the mentioned
CNNs and support vector machine has been used for binary classification. Among
linear and nonlinear dimensionality reduction algorithms, linear approaches
such as principal component analysis (PCA) represent a better combination among
deep features and lead to a higher accuracy in the classification task using
small number of features considering specific amount of cumulative explained
variance (CEV) of features. The proposed method is validated using experimental
BreakHis dataset. Comprehensive results show improvement in the classification
accuracy up to 4.3% with less computational time. Best achieved accuracy is
91.13% for 400x data with feature vector size (FVS) of 23 and CEV equals to
0.15 using pre-trained AlexNet as feature extractor and PCA as feature
reduction algorithm
A review and comparative study of cancer detection using machine learning : SBERT and SimCSE application
AVAILABILITY OF DATA AND MATERIALS : The data can be accessed at the host database (The European Genome-phenome Archive at the European Bioinformatics
Institute, accession number: EGAD00001004582 Data access).BACKGROUND : Using visual, biological, and electronic health records data as the sole
input source, pretrained convolutional neural networks and conventional machine
learning methods have been heavily employed for the identification of various malignancies.
Initially, a series of preprocessing steps and image segmentation steps are
performed to extract region of interest features from noisy features. Then, the extracted
features are applied to several machine learning and deep learning methods for the
detection of cancer.
METHODS : In this work, a review of all the methods that have been applied to develop
machine learning algorithms that detect cancer is provided. With more than 100 types
of cancer, this study only examines research on the four most common and prevalent
cancers worldwide: lung, breast, prostate, and colorectal cancer. Next, by using
state-of-the-art sentence transformers namely: SBERT (2019) and the unsupervised
SimCSE (2021), this study proposes a new methodology for detecting cancer. This
method requires raw DNA sequences of matched tumor/normal pair as the only input.
The learnt DNA representations retrieved from SBERT and SimCSE will then be sent to
machine learning algorithms (XGBoost, Random Forest, LightGBM, and CNNs) for classification.
As far as we are aware, SBERT and SimCSE transformers have not been applied
to represent DNA sequences in cancer detection settings.
RESULTS : The XGBoost model, which had the highest overall accuracy of 73 ± 0.13 %
using SBERT embeddings and 75 ± 0.12 % using SimCSE embeddings, was the best
performing classifier. In light of these findings, it can be concluded that incorporating
sentence representations from SimCSE’s sentence transformer only marginally
improved the performance of machine learning models.The South African Medical Research Council (SAMRC) through its Division of Research Capacity Development under the Internship Scholarship Program from funding received from the South African National Treasury.https://bmcbioinformatics.biomedcentral.comam2024Computer ScienceSchool of Health Systems and Public Health (SHSPH)Non