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Colorectal cancer (CRC) is the second most dangerous type of cancer in terms of causing deaths in patients and third most common type of cancer found in people in terms of incidence. CRC can be further categorized based on its molecular subtypes. Each subtype displays different features. Thus, identifying molecular subtypes of CRC and treating …
The goal of this analysis is to explore the machine learning-based automatic diagnosis of colorectal patients based on the single nucleotide polymorphisms (SNP). Such a computational approach may be used complementary to other diagnosis tools, such as, biopsy, CT scan, and MRI. Moreover, it may be used as a low-cost screening for colorectal cancers
Colorectal cancer (CRC) is the second most dangerous type of cancer in terms of causing deaths in patients and third most common type of cancer found in people in terms of incidence. CRC can be further categorized based on its molecular subtypes. Each subtype displays different features. Thus, identifying molecular subtypes of CRC and treating p…
Developed a fine-tuned EfficientNetB0 model which is a pre-trained Convolutional Neural Network (CNN) model to train using lungs and colon cancer dataset and classify if the unseen image belonged to benign, adenocarcinoma or squamous cell carcinoma cancer type.
Codes for parameter estimation and sensitivity analysis of QSP models for colon cancer. This is a part of the National Cancer Institute funded project titled "Data-driven QSP software for personalized colon cancer treatment" Achyuth Manoj, Susanth Kakarla, Suvra Pal and Souvik Roy.