Papers by Rajasekaran Subramanian PhD PMP
IP Journal of Diagnostic Pathology and Oncology
The evaluation of lymph nodes' metastasis is an important component of Tumor, Node, Metastasis (T... more 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.
Automatic carcinoma identification from breast epithelial tissue WSI through U-Net deep learning network
IP Journal of Diagnostic Pathology and Oncology
Frequent Contiguous Pattern Mining Algorithms for Biological Data Sequences
HeurisFCP — A heuristic approach to Identify Frequent Contiguous Patterns (FCP) in Sequence Database
Identifying Frequent Contiguous Patterns(FCP) involvesfinding the frequently repeated contiguous ... more Identifying Frequent Contiguous Patterns(FCP) involvesfinding the frequently repeated contiguous pattern in Sequence Database (SDB). The existing FCP Algorithms have drawbacks of repeated scanning of Database which require high computation time and large storage space. In this paper, a heuristic algorithmhave been presented which minimizes scanning the time of the SDB and also provides clues for finding the FCP in a SDB. This Algorithm helpsin many SDB applications such as to extract Motif or Regulatory Regions in Genomic SDB, or to find repeated requiring items in the InventorySDBor to find stock trading patterns in Stock Trading SDB, etc.
A Study on Recent Algorithms for Multiple Longest Common Subsequence (MLCS) Problem in Biological Sequences Comparisons
Design and Analysis of Efficient Data Processing Paradigm for Quantitative Finance
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Papers by Rajasekaran Subramanian PhD PMP