## 2 Materials and Methods
### AlexNet  
AlexNet is a convolutional neural network that is 8 layers deep developed in 2012 by [PERSON] et al. [17]. In 2012, AlexNet entered the ImageNet Large Scale Visual Recognition Challenge. The network had a top-five error rate [17]. Against all classic machine learning and computer vision approaches, AlexNet obtained state-of-the-art recognition accuracy. It was a watershed moment in the history of machine learning and computer vision for visual recognition and classification tasks, and it marked the beginning of a surge in interest in deep learning [18]. To successfully speed model convergence, AlexNet first uses rectified linear units (ReLU) as the activation function. Overfitting is avoided via the dropout function, and the addition of a local response normalisation (LRN) layer improves generalisation [19]. AlexNet's architecture is depicted in Table S2 [20]. Because of its simple network structure and modest depth, AlexNet has been widely employed [19]. Figure 3: Samples of colour mapping (**a**) original normal image, (**b**) colour mapped normal image, (**c**) original benign image, (**d**) colour mapped benign image, (**e**) original malignant image, (**f**) colour mapped malignant image. GoogleNet  
GoogleNet is a 22-layer deep convolutional neural network developed by Google researchers as a variation of the inception network, a deep convolutional neural network. The GoogleNet architecture's input layer handles an image with a size of 224 \(\times\) 224. The GoogleNet architecture was established to be a powerhouse with more computational efficiency than some of its predecessors or similar networks at the time [21]. The total number of layers (independent building pieces) involved in the network's construction is around 100. The actual amount relies on how the machine learning infrastructure counts layers [22]. The inception module is the key feature of this net, and it helps to reduce the overall number of parameters. It also employs average pooling layers at the end of the net instead of full-connected layers, and it only preserves the final full-connected layer before the classification layer [21]. Table S3 shows GoogleNet's architecture. VGG-16  
[PERSON] and [PERSON] of Oxford University's Visual Geometry Group Lab proposed VGG-16 in 2014 [23]. VGG-16 is a 16-layer model with a huge number of weight parameters. In the first and second convolutional layers, VGG-16 replaces AlexNet's \(11\times 11\) and \(5\times 5\) filters with numerous \(3\times 3\) kernel-sized filters (the smallest size to capture the values of left/right, up/down, and centre). This model contains over 138 million parameters and is over 500 megabytes in size [23]. MobileNetV2  
MobileNet is a type of convolutional neural network that is intended for use in mobile and computer vision applications [24]. MobileNets are built on a simplified design that builds lightweight deep neural networks using depth-wise separable convolutions [24]. The building elements of the MobileNetV2 model are based on the previous version, MobileNetV1, and use depthwise separable convolution. MobileNetV2 adds features such as linear bottlenecks between layers and shortcut connections between bottlenecks [25]. Table S4 shows the architecture of MobileNetV2 including the initial fully convolution layer with 32 filters, followed by 19 residual bottleneck layers [26]. DenseNet  
DenseNet is a recently developed model primarily to address the vanishing gradient's effect on high-level neural networks' accuracy. The information evaporates before it reaches its destination due to the long journey between the input and output layers. DenseNet is quite like ResNet, although there are a few key distinctions. DenseNet concatenates the output of the previous layer with the output of the future layer, whereas ResNet utilises an additive approach that combines the previous layer with the upcoming layer [27]. Mod_AlexNet  
AlexNet is a well-known deep learning architecture in the field of medical image analysis.