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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.
scMalignantFinder is a Python package specially designed for analyzing cancer single-cell RNA-seq datasets to distinguish malignant cells from their normal counterparts.
Learning Vector Quantization ( or LVQ ) is a type of Artificial Neural Network which also inspired by biological models of neural systems. It is based on a prototype supervised learning classification algorithm and trained its network through a competitive learning algorithm similar to Self Organizing Map.
This work aims to analyze data corresponding to patients diagnosed with breast cancer, apply data mining to predict disease recurrence, and compare the performance of machine learning techniques in breast cancer relapse classification.
A comprehensive Jupyter notebook project that uses Support Vector Machines (SVM) for the classification of breast tumors into malignant or benign categories. The notebook includes data exploration, visualization, model training, and evaluation, providing insights into breast cancer diagnosis using machine learning.