## 2 Related Work  
[PERSON] and [PERSON] [30] used the Xception and VGG16 structures to fine-tune transfer learning. The design of Xception was substantially altered with the addition of two fully linked levels and multiple-output tiers with a SoftMax activation mechanism. As per the theory, the channel's initial layer has the greatest potential for generality. The previous eight layers of the VGG16 architecture have been stopped, and the fully linked levels have been altered. Similarly, the testing periods for every image were 16 ms for the VGG16 and 20 ms for the Xception network. The methods used in [31] included InceptionV3, ResNet18, and GoogLeNet. A conclusion was reached by using convolutional networks. To see whether a vote might be utilized to come to an accurate diagnosis, they tested each model against the premise. The classifier results were merged by using the strong majority in this investigation. This meant that the diagnosis went with the group that had a high proportion of first-time voters. According to the average of this model's testing results, this approach took an average of 161 ms per image. On top of that, they were able to classify X-ray images with great accuracy. Pneumonia could be detected using deep CNNs, as per the results of this research. We used standard algorithms as a component in our approach to categorize data in order to keep computation costs at a minimum. The results were obtained in many test sets by utilizing bi-linear classification methods and SVM classifiers to extract features from the VGG and ResNet models [32]. About 13 W of dermatological images were trained using a mix of data-driven methods and InceptionV3, with results in the test dataset that were similar to those of dermatologists, as reported in [33]. The ISBI-2016 skin lesion analysis approach for cancer diagnosis [34] used skin lesion fragmentation to classify cancer. As a consequence, a move-categorization process was recommended for classification purposes. The use of several convolutional neural networks (CNNs) combined with dynamic pattern training simulated intra-class fighting amongst cancer cells and the background noise that resulted from this [35]. Instead of starting from scratch with random initialization settings, [PERSON] et al. [36] used a pre-trained CNN to detect skin images across a whole dataset. After this pre-training, the CNN's training time was cut in half, resulting in an accuracy rate of 84.8% for five different courses. [PERSON] et al. [37] used a DL technique for early diagnosis. TL was used to create this VGGNet-based model. The generated model had a primary collection of 78.56% when used with the ISIC-2016 archive data. The expanded and un-augmented data from [38] were used to evaluate the performance of a CNN model for the detection of lesions. According to the authors, DL methods may be beneficial, but there is little evidence to support them. A more significant dataset improved the network performance of the classifier over a model that did not have it. CNNs have been widely used in medical image analysis in recent years because of their broad feature extraction abilities, which have shown impressive results. [PERSON] et al. [34] presented a multi-stage residual-network-based approach to automatically detecting malignant tumors in dermoscopic images in order to identify melanomas. VGG and ResNet networks bi-linearly combined with SVM classifiers yielded some of the highest detection scores on several testing datasets, with high-level information being collected by [PERSON] et al. [32]. Based on [39], a multi-level fully convolutional network was built. Using multi-CNN cooperation, [PERSON] et al. [40] created a model for identifying target class lesions. Their method was more accurate in detecting lesions, and its usefulness was tested with the right information. It was possible to build a robust ensemble structure for early cancer diagnosis by using dynamic classification methods. Thus, we could build an improved and distinguishable model.