Abstract :
The evaluation of lymph nodes’ metastasis is an important component of Tumor, Node, Metastasis
(TNM) breast cancer staging system for better clinical management and treatment. Assessing lymph node
metastasis through histologic examination is the most accurate method. This paper proposes significantly
advanced and faster image classification Convolutional Neural Network (CNN) model called Densenet-161
for lymph node metastasis. This paper uses pre-processing technique called image thresholding to improve
the contrast intensities of the SLN images, which improves the performance of DenseNet. The experimental
PCam dataset contains 327,680 patches extracted from 400 Haemotoxylin and Eosin (H&E) stained WSIs
of breast cancer with sentinel lymph node sections. The proposed system has generated 94% accuracy for
lymph node metastasis classification.
Context: Automatic, faster and accurate computational technique implementation for breast cancer
sentinel lymph metastases classification in cancer diagnostic pathology.
Aims: Automatic sentinel lymph node metastases classification on breast carcinoma WSI through deep
learning network, DenseNet-161.
Settings and Design: Tumor cells are migrating from a primary metastasize to one or a few lymph nodes,
before spreading to other lymph nodes. These few lymph nodes are called as ”sentinel” lymph nodes.
The status of these sentinel lymph nodes would accurately predict the status of the remaining lymph nodes.
Lymph node status assessment is considered to be one of the most important independent prognostic factors
in breast cancer.
Methods and Materials: Breast cancer metastasis spreads the tumor cells to other parts of the body,
which is predominantly through sentinel lymph node. This paper uses an image classification model
DenseNet-161 to classify metastaized and normal SLN images. The DenseNets are significantly advantaged
over traditional CNNs, by reducing the vanishing-gradient problem, having feature reusage, strengthening
feature propagation, having significant reduction in number of parameters and less computation time. The
experimental dataset contains
Statistical analysis used: The performance of the Densenet-161 is measured by statistic parameter F1
score, training and validation accuracy.
Results: The proposed system has generated a training accuracy of 0.9477 and validation accuracy of
0.944, with an F1 score of 0.8406.
Conclusions: This model involves extraction of complex information from the medical images dataset,
which requires the removal of noise. Even after applying thresholding pre-processing method the noise
persists, which requires additional pre-processing before training the model. And by increasing the dataset
size through data-augmentation will also improve the accuracy considerably.
Keyword :
Sentinel Lymph Node, Axillary Lymph Node, Metastasis, Metastasized Breast Cancer, DenseNet, CNN, Diagnostic Pathology, Digital Pathology, WSI Images.