Multiclass classification model using a custom convolutional neural network for accurately detecting melanoma.
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Updated
Jun 27, 2023 - Jupyter Notebook
Multiclass classification model using a custom convolutional neural network for accurately detecting melanoma.
Find breast cancers in screening mammograms
CT Scan Lung Cancer Detection
Histopathologic metastatic breast cancer detection with convolution neural networks on pathology whole slide images using TensorFlow.
My research poster presentations
An algorithm to detect if a cancerous tumour is malignant or benign based on attributes data in the form of .CSV files.
🏥 ISIC - Skin Cancer 🔎 Exploratoy Data Analysis
Predicting if a mass detected in a mammogram is benign or maligant ,on the basis of that we can easily tell that whether its sign of cancer or not. previously this work is done using seeing the mammogram image manually by doctor predicting whether its maligant or not but now we are using Machine learning model to learn from previous patient data…
cancer detection from histopathological images
Create a model which can determine if the patient has cancer or not. This project was done with Vidya Durai (BNY Mellon), during my second year of college.
In Testing - comments welcome. Tool to provide guidance on colonoscopic surveillance based on BSG/PHE/ACPGBI 2019 surveillance guidelines and BSG hereditary cancer guidelines.
Breast Cancer Classififer Model ( ML ) With Hyper Parameter Tuning 97% Accuracy
🔍 Project to learn how to cooperate with image database in Deep Learning. Creating a model to skin diseases detection.
Breast cancer classification project.
Undergrad Thesis on Yolo based colorectal polyp detection with GUI
Prediction Using Linear Regression Models of Least Squares, Ridge Regression, and Lasso Regression
Pre-Processing Segmentation Datasets for the Hydra Framework
Kanser Türleri Ayırt Etmek Amacıyla Parçacık Sürüsü Optimizasyonu Kullanılarak Konvolüsyonel (Evrişimli) Modelleri En Efektif Şeklide Eğitme
We use SVM (Support Vector Machines) to build and train a model using human cell records, and classify cells to whether the samples are benign or malignant.
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