Organize and add seg model

Classification Sample Models/lymphoma-cancer-classification/README.md

@@ -12,16 +12,21 @@ Non-overlapping patches of size 256 x 256 are extracted from each image. 15% fro
 ### Inception Recurrent Convolutional Neural Network
 The IRCNN architecture consists of general convolution layers, IRCNN blocks, transaction blocks, and a softmax logistic regression layer.
 <img src="imgs/lymph.jpg">
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 The IRCNN block, performs recurrent convolution operations on different sized kernels.
 <img src="imgs/arch.png">
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 As the input and output dimensions do not change, this is simply an accumulation of feature maps with respect to the time step considered. This helps to strengthen the extraction of the target features.
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 In the transaction block, three operations (convolution, pooling, and drop-out) are performed depending upon the placement of the block in the network. According to Figure,  all of the operations are applied in the very first transaction block and second transaction block. The third transaction block consists of convolution, global-average pooling, and drop-out layer. The GlobalAveragePooling layer is used as an alternative to a fully connected layer.
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 The Stochastics Gradient Descent (SGD) optimization method is used with initial learning rate 0.01
 ### Usage
-The models folder contains Keras model and it's corresponding tensorflowjs model. Download the .zip file of repository and load the model in caMicrscope.
+The *model* folder contains Keras model and it's corresponding tensorflowjs model. Download the .zip file of repository and load the model in caMicrscope.
 ### References
-- Advanced Deep Convolutional Neural Network Approaches for Digital Pathology Image Analysis: a comprehensive evaluation with different use cases
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+- *Advanced Deep Convolutional Neural Network Approaches for Digital Pathology Image Analysis: a comprehensive evaluation with different use cases*
 Md Zahangir Alom, Theus Aspiras, Tarek M. Taha, Vijayan K. Asari, TJ Bowen, Dave Billiter, Simon Arkell
-- M. Liang, X. Hu, "Recurrent convolutional neural network for object recognition", CVPR, pp. 3367-3375, 2015.
-- Inception Recurrent Convolutional Neural Network for Object Recognition
+- M. Liang, X. Hu, *"Recurrent convolutional neural network for object recognition"*, CVPR, pp. 3367-3375, 2015.
+- *Inception Recurrent Convolutional Neural Network for Object Recognition*
 Md Zahangir Alom, Mahmudul Hasan, Chris Yakopcic, Tarek M. Taha