# Reviewing CAM-Based Deep Explainable Methods in Healthcare
## 5 Experiment
### Classification Models  
To better validate the effectiveness of CAM methods, this study selected GoogleNet [70], ResNet50 [61], DenseNet121 [71], and EfficientNet-B3 [62] as the four models because they represent different deep learning architectural paradigms in the area of computer vision. Each model possesses a unique structure and working principle, offering diverse perspectives for understanding and interpreting visual data. Due to their structural differences, these models vary in their methods of extracting and processing image features, which may result in different focal points and interpretative patterns when CAM methods are applied. By employing CAM methods on these diverse models, we can explore and compare their performance across various architectures, thereby enhancing the generalizability and practicality of the research findings. DenseNet121, developed in 2016 by [PERSON] and others, is a DenseNet series deep learning model for image classification and computer vision. Its key feature is dense connections in the network, facilitating information flow and allowing deeper structures. In our study, we used this model to complete the classification tasks for five datasets, generating corresponding confusion matrices as illustrated in Figure 8.  
EfficientNet-B3, part of the EfficientNet series, is a scaled deep neural network model optimized for performance and complexity. It's distinct for balancing depth, width, and resolution efficiently, enabling high performance with smaller size and less computational demand. In our study, it was applied to classification tasks on five datasets. The confusion matrices generated from these classifications are presented as Figure 9. These matrices visually depict the performance of the model, highlighting the accuracy of predictions across different classes within each dataset. Confusion matrices are particularly useful for identifying classes that the model may find challenging to classify correctly. Figure 8: Densenet121 confusion matrix. ((**a**–**e**) represent the confusion matrices obtained from the classification tasks performed by DenseNet121 on datasets HAM10000, Diabetic Retinopathy. Arranged, Breast Ultrasound Images Dataset, Brain Tumor Classification (MRI), and Chest X-ray Images, respectively). ResNet50 is an architecture within the deep convolutional neural network framework and is a part of the ResNet series. The ResNet (Residual Network) series is renowned for its innovative residual connections; these were developed to tackle the issues of vanishing and exploding gradients, commonly encountered during the training of deep convolutional neural networks. ResNet-50, with its 50 layers, has shown exceptional performance in image classification and computer vision tasks. In our research, ResNet-50 was employed to classify five different datasets. The corresponding confusion matrices generated from these classification tasks are presented in Figure 10. These matrices visually depict the model's accuracy and effectiveness in correctly classifying the images from each dataset. ResNet-50's deep layered structure, combined with residual connections, allows it to learn complex features without losing important information through the depth of the network, thus making it a powerful tool for such tasks. Figure 9: Efficientnet_b3 confusion matrix. ((**a**–**e**) represent the confusion matrices obtained from the classification tasks performed by EfficientNet-B3 on datasets HAM10000, Diabetic Retinopathy Arranged, Breast Ultrasound Images Dataset, Brain Tumor Classification (MRI), and Chest X-ray Images, respectively). GoogleNet, or Inception network, introduced in 2014 by Google, marked a significant development in convolutional neural networks. It integrated a novel structure, the inception module, to enhance performance and efficiency. This module features parallel convolutions of varying scales for capturing diverse spatial information, boosting the network's depth and width with minimal parameter increase.