Projects

• Skin Cancer Classification: Machine Learning vs Deep Learning Techniques (November 2024)

  Developed a Computer-Aided Diagnostic (CAD) system for skin lesion classification using advanced preprocessing techniques and feature extraction. Implemented traditional machine learning models for binary and multiclass classification of skin lesions, distinguishing between benign and malignant cases. Future work includes exploring deep learning approaches for improved accuracy.

  Keywords: Skin Cancer Classification, Machine Learning, Feature Extraction, Image Processing, Binary Classification, Multi-class Classification, Benign, Malignant

  

  

  

  

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  Code Presentation

• Unsupervised Brain Tissue Segmentation with GMM and EM (October 2024)

  Implemented an unsupervised pipeline for brain tissue segmentation using Gaussian Mixture Models (GMM) and Expectation-Maximization (EM). The pipeline utilizes k-means clustering for initialization and refines segmentation into Grey Matter (GM), White Matter (WM), and Cerebrospinal Fluid (CSF). The method was evaluated using Dice similarity scores, highlighting challenges and improvements across T1 and T2_FLAIR MRI images.

  Keywords: Brain Tissue Segmentation, MRI, Gaussian Mixture Models, Expectation-Maximization, Dice Similarity, Medical Imaging, Grey Matter, White Matter, CSF

  

  

  

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  Code Report

• Multi-class Classification and Gland Segmentation of Colorectal Cancer Tissues from Histopathology Images (June 2024)

  Developed a comprehensive pipeline combining advanced image processing techniques, such as K-means clustering and Watershed algorithm, for precise gland segmentation. Implemented classification of colorectal cancer tissues into multiple categories using both traditional machine learning and deep learning models, achieving high accuracy and robustness in segmentation and classification tasks.

  Keywords: Colorectal Cancer, Histopathology Image Classification, Gland Segmentation, Image Processing, Machine Learning, Deep Learning

  

  

  

  

  

  

  

  

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  Code Report Presentation

• Stock Trend Prediction using Machine Learning (May 2024)

  Developed a robust machine learning model to predict stock market trends using historical data from 300 companies across 11 sectors. Employed advanced data preprocessing techniques, including handling missing values with methods like median imputation and missForest. Various machine learning models, such as Random Forest, LightGBM, and Histogram Gradient Boosting, were utilized. The final model, enhanced with hyperparameter tuning using Optuna, achieved a notable error score of 0.7921.

  Keywords: Stock Trend Prediction, Machine Learning, Data Preprocessing, Time Series Analysis, Random Forest, LightGBM, Histogram Gradient Boosting, Optuna, Financial Indicators, Imputation Techniques

  

  

  

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  Code Presentation

• LLM Generated Text Detection System (December 2023)

  Developed an AI model to detect and classify text within digital documents using Natural Language Processing (NLP) and machine learning techniques. The project aimed to improve the accuracy of text detection in various document formats, including scanned images and PDFs.

  Keywords: Artificial Intelligence, Text Detection, Natural Language Processing, Machine Learning

  

  

  

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  Code Application