MY_PROJECTS

 

IDCBreastCancer_histopathologyImages_deepResidualLearning

IDC prediction in breast cancer histopathology images using deep residual learning with an accuracy of 99.37% in a subset of images containing a total of 7,500 microscopic images. The model link – Chandra-cc/GitHub

Pneumonia-detection-using-deep-residual-learning-along-with-separable-CNN

Pneumonia is an infectious disease that inflames the air sacs in the lungs. It may occur in one or both the lungs. The air sacs may fill with fluid or pus, causing cough followed by phlegm or pus, fever, chills and difficulty in breathing. The causes of pneumonia can be a variety of organisms such as bacteria, viruses and fungi. A person suffering from pneumonia apart from suffering from difficulty in respiration, can also suffer from other complications such as bacteremia, lung abscess, pleural effusion and among countless others. This model presents a efficient and enhanced method for diagnosis of pnuemonia using deep residual learning along with seperable convolutional networks which achieved an astonishing accuracy of 98.22%.
The images were CXR (Chest-X-ray images) in grayscale format obtained from the mendeley CXR data available at kaggle Dataset Link

The model link – Chandra-cc/Github

Object_Classification_Deep_Residual_Seperable_CNN_with_base_model_VGG16

Object Classification using VGG16 Network on cifar10 datasets from keras.
The images were imported from keras.datsets containing about 50000 training imaages with 10000 testing images.
The model was created with VGG16 as its base model and a Residual Seperable Convolutional layer following it.
The optimzer was Adam, 50 epochs with batch size of 16 and early stopping with patience of 10 and lr_reduce with factor of 0.1 and patience of 2 were applied to the network.
The training set was split into validation set and tgraining set itself with a validation split = 0.1
The link of the Model – Chandra-cc/GitHub

Object-Classificarion-Resnet50

The project involves object classification from Resnet50 on the cifar dataset. link – object_classifiacation

 

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Emotion Recognition From Speech Signals -Emotion Recognition using matlab,Machine Learning using SVM (Support vector Machine).

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Suggestive dictionary based on Speech- A speech based dictionary.

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Star wars Another python game using Pygame.Capture1

Python Game -“Racey”– A 2D game using python and pygame.

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Face-detection– Face detection using python and pandas.

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Motion-Detection-Motion detection using Webcam developed using python and pandas and image and video processing

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Interactive-Dictionary-Dictionary with suggestion for wrong spellings, developed using python script and file handling with Jason.

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Website-Blocker-Website URL’s blocked by writing them into a file using file handling with python.

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Webmap-Population-A web map representing population density and important places developed using file handling with python.

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GUI-Data-handling-A data management GUI based application to help in operating data.

 

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