## 3 The Proposed Model
### Disease Classification Model
#### 3.4.1 Feature Extraction Using SqueezeNet  
To produce feature vectors, the SqueezeNet model is used in this work. SqueezeNet is a small, efficient deep neural network structure [26]. It is intended to be faster and smaller than other architectures while accomplishing higher accuracy on image classification tasks. SqueezeNet exploits a technique named \"squeeze-and-excitation\" to decrease the parameter count in the network, enabling it to be more effective in terms of memory and computational requirements. It was specifically designed for use on embedded and mobile devices. The SqueezeNet architecture comprises a series of layers, including the following:* Pooling layers, decrease the spatial dimension of the feature maps. * The input layer takes in the image dataset. * A sequence of FC layers categorises the image based on the extracted features through the convolution layer. * A sequence of convolutional layers extracts features from the image. This layer uses \"fire modules\" that comprise the \"expand\" layer with a larger number of filters, followed by \"squeeze\" layer with smaller number of filters. This enables the network to maintain fewer parameters while still extracting relevant features from the image. The architecture also includes a dropout layer and a softmax layer. Dropout is a regularization technique that prevents overfitting by randomly dropping out some neurons during training. The softmax layer creates a probability distribution over the feasible classes used to classify the image. The SqueezeNet architecture is designed to be small and efficient, with a total of only 1.2 million parameters. This makes it better suited for use on embedded and mobile devices with limited computational resources. Here, the BO is utilized for the hyperparameter selection of SqueezeNet. A probabilistic method of the objective function \((x)\) called the surrogate function can be created using BO technique [27]. Bayes' Theorem is the core of BO technique and computes the conditional probability of event \(A\) given another event \(B,\ P(A|B)\),  
\[P(A|B)=P(B|A)*\frac{P(A)}{P(B)} \tag{16}\]  
[PERSON] theorem is capable of being adjusted by neglecting the \(P(B)\) marginal probability from the conditional probability for optimization problem using Equation (17):  
\[P(A|B)=P(B|A)*P(A) \tag{17}\]  
A new version of [PERSON] theorem is given as follows:  
\[Posterior=Likelihood\ *Prior \tag{18}\]  
The conditional probability is a function that estimates the objective function and is utilized for sampling the searching space. The searching space in these problems is the SqueezeNet hyperparameter. BO algorithm, as an informed search technique, can be differentiated by using an acquisition function that exploits the posterior for sampling the searching area and selecting the succeeding sample to evaluate the objective function. In this study, the objective function can be denoted as a Gaussian method with the Matern 5/2 kernel. At first, random samples from the search space \((x_{1},\ x_{2},\ \ldots,\ x_{n})\) are exploited for determining the \(F(x)\) objective function at these samples. The assessments and samples are gathered in a consecutive way, which leads to a sequence of data points \(S=\left\{x_{i},\ F(x_{j}),\ \ldots x_{n},\ F(x_{n})\right\}\), where \(n\) denotes the number of samples. The set \(S\) defines the likelihood, and prior function can be described as follows:  
\[P(F|S)=P(S|F)*P(S) \tag{19}\]  
The posterior is updated afterwards with the evaluation of prior and probability. The acquisition function, \(C\), is enhanced over the Gaussian algorithm for selecting the subsequent sample \(x_{n}\), which can be represented as follows:  
\[x_{n}=\ \operatorname{argmax}_{x}C(x|S_{1:n-1}) \tag{20}\]  
In this study, the acquisition function can be executed by the expected improvement algorithm, as defined below.