## 3 Algorithm Overview  
The proposed FCN model is based on the encoder and decoder network, which is inspired from VGG-16 FCN and is based on the initial 13 convolution layers. The decoder is inspired from SegNet [40], which is used to up-sample every input by the encoder network through pooling layers. Each decoder carries out a non-linear up-sampling to construct complete feature maps from sparse max-pooling indices produced at each pooling layer in the encoder network. The proposed network is a combination of two decoupled FCN networks, which perform their respective jobs individually. The encoder takes image inputs from the training set, while the decoder takes sparse max-pooling indices from encoder pooling layers. In [39], an up-sampling architecture is proposed, which provides a base for our FCN decoder for unsupervised feature learning. The decoding process is the key aspect of the proposed model, which offers a number of practical benefits regarding enhancement of boundary delineation and minimization. It also offers a great deal of improvement regarding reducing the amount of trainable parameters to enable end-to-end training. The main aspect of our proposed decoupled architecture is the easy modification and simple training with different environment variables. The encoder produces low-resolution feature maps for pixel-wise classification, while the decoder up-samples them by convolving the trainable filters to harvest dense feature maps. The key distinctive features of the proposed CCN model are:  
* It is a novel encoder-decoder-based semantic pixel-wise segmentation engine. * It offers a simple training process, which simultaneously trains encoder and decoder networks. * It significantly reduces the memory consumption due to the smart decoder architecture. * It employs the latest activation function to improve the potential performance. * This offers a flexible architecture to tune any size of input images. Algorithms 1 and 2 are proposed respectively for the encoder and decoder of CSSA, respectively. These algorithms are outlined below for the detailed overview of the proposed CNN network operations. Algorithm 3 presents the batch normalization function. **Algorithm 1** Training a CNN encoder: The network will accept input \(F_{i}\) having a volume of size \(X_{1}\times H_{1}\times D_{1}\). It requires hypermeters, \(K\) number of filters, \(SE\) spatial extent, \(S\) stride rate and \(P\) amount of zero padding. The size of output volume is \(X_{2}\times H_{2}\times D_{2}\). Here, \(C\) denotes the cost function for mini-batch. The learning rate decay factor is denoted by \(\lambda\), and the numbers of layers are represented as \(L\). \(BatchNorm()\) denotes the batch-normalize activations on given parameters. The \(ELU()\) performs the exponential linear units' activation of the given input. The next function \(Conv()\) performs the convolution operations with a constant rate of \(kernelsize=3\), \(S=1(stride)\) and \(P=1(zeropadding)\). The \(Pool()\) performs the pooling operation with Max pooling filter and a constant rate of \(kernelsize=2,S=2(stride)\) and \(P=0(zero\ padding)\). ```
0: a mini-batch of inputs and targets \((a_{0},a_{*})\), previous weights \(W\), previous BatchNorm parameters \(\theta\), weights initialization coefficients from \(\gamma\), and previous learning rate \(\eta\). Employed the [PERSON] et al. 's [45] proposed method for network initialization weight parameters. 0: updated weights \(W^{t+1}\), updated BatchNorm parameters \(\theta^{t+1}\) and updated learning rate \(\eta^{t+1}\). 1. Encoder Computations : 1.1.