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medicalai.chief.networks

NetworkInit

NetworkInit()
Base class for parameter Network initializers.

The :class:NetworkInit class represents a network initializer used to initialize network/model parameters for numerous medical ai networks. It should be subclassed when implementing new types of network initializers.

call

NetworkInit.call(inputSize, OutputSize, convLayers=None)
Sample should return model initialized with input and output Sizes.

Parameters

inputSize : tuple or int. Integer or tuple specifying the input of network.

OutputSize : tuple or int. Integer or tuple specifying the output classes of network.

Returns

numpy.array. Initialized Model.

tinyMedNet

tinyMedNet()
tinyMedNet is a classification network that consumes very less resources and can be trained even on CPUs. This network can be used to demonstrate the framework working. Additionally this acts a starting point for example/tutorial for getting started to know the Medical AI library.

tinyMedNet_v2

tinyMedNet_v2()
tinyMedNet_v2 allows users to configure the number of Conv/CNN layers. tinyMedNet_v2 is a classification network that consumes very less resources and can be trained even on CPUs. This network can be used to demonstrate the framework working. Additionally this acts a starting point for example/tutorial for getting started to know the Medical AI library.

tinyMedNet_v3

tinyMedNet_v3()
tinyMedNet_v3 has 3 FC layers with Dropout and Configurable number of Conv/CNN Layers.

resNet20

resNet20()
resnet20

resNet32

resNet32()
resnet32

resNet56

resNet56()
RESNET56

resNet110

resNet110()
resnet110

megaNet

megaNet()

megaNet is based on COVID-NET. This is a tensorflow 2.0 network variant for COVID-Net described in Paper "COVID-Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest Radiography Images" by Linda Wang et al. Reference: https://github.com/busyyang/COVID-19/

DenseNet121

DenseNet121()

DenseNet121 model, with weights pre-trained on ImageNet inputSize: input image size tuple outputSize: Number of classes for prediction

VGG16

VGG16()

VGG16 model, with weights pre-trained on ImageNet inputSize: input image size tuple,default : (224,223,3) outputSize: Number of classes for prediction

MobileNet

MobileNet()

MobileNet model, with weights pre-trained on ImageNet inputSize: input image size tuple,default : (224,223,3) outputSize: Number of classes for prediction

MobileNetV2

MobileNetV2()

MobileNet model, with weights pre-trained on ImageNet inputSize: input image size tuple,default : (224,223,3) outputSize: Number of classes for prediction

Xception

Xception()

Xception model, with weights pre-trained on ImageNet inputSize: input image size tuple,default : (224,223,3) outputSize: Number of classes for prediction

InceptionV3

InceptionV3()

InceptionV3 model, with weights pre-trained on ImageNet inputSize: input image size tuple,default : (224,223,3) outputSize: Number of classes for prediction

InceptionResNetV2

InceptionResNetV2()

InceptionResNetV2 model, with weights pre-trained on ImageNet inputSize: input image size tuple,default : (224,223,3) outputSize: Number of classes for prediction