Pinned Repositories
Cervical_Cancer
Cancer is a leading cause of death worldwide, accounting for approximately 8.8 million deaths a year. It arises from the transformation of normal cells into tumor cells in a multistage process that generally progresses from a pre-cancerous lesion to a malignant tumor. Cervical cancer is the fourth most frequent cancer in women. There are various algorithms and methodologies used for automated screening of cervical cancer by segmenting and classifying cervical cancer. The proposed system uses deep convolutional neural network to perform image classification by accepting an input image, processing it and classifying it under specified categories. Convolutional Neural Network is highly preferred because of its architecture and also the best feature is that it does not have feature extraction. We have built different models such as Alexnet, Squeezenet, VGG, Resnet50, Resnet101 and Densenet201. It was observed that Densenet201 gave us the highest accuracy of 97%.
Clustering_Algorithms_from_Scratch
Implementing Clustering Algorithms from scratch in MATLAB and Python
Dlithe-Internship
iris_dataset
Clustering techniques for IRIS Dataset Classification
MNIST-DATASET
python_for_microscopists
https://www.youtube.com/channel/UC34rW-HtPJulxr5wp2Xa04w?sub_confirmation=1
shrihnayak's Repositories
shrihnayak/Clustering_Algorithms_from_Scratch
Implementing Clustering Algorithms from scratch in MATLAB and Python
shrihnayak/Dlithe-Internship
shrihnayak/Cervical_Cancer
Cancer is a leading cause of death worldwide, accounting for approximately 8.8 million deaths a year. It arises from the transformation of normal cells into tumor cells in a multistage process that generally progresses from a pre-cancerous lesion to a malignant tumor. Cervical cancer is the fourth most frequent cancer in women. There are various algorithms and methodologies used for automated screening of cervical cancer by segmenting and classifying cervical cancer. The proposed system uses deep convolutional neural network to perform image classification by accepting an input image, processing it and classifying it under specified categories. Convolutional Neural Network is highly preferred because of its architecture and also the best feature is that it does not have feature extraction. We have built different models such as Alexnet, Squeezenet, VGG, Resnet50, Resnet101 and Densenet201. It was observed that Densenet201 gave us the highest accuracy of 97%.
shrihnayak/iris_dataset
Clustering techniques for IRIS Dataset Classification
shrihnayak/python_for_microscopists
https://www.youtube.com/channel/UC34rW-HtPJulxr5wp2Xa04w?sub_confirmation=1
shrihnayak/MNIST-DATASET