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