ABSTRAT:
Monkeypox virus is emerging slowly with the decline of COVID- 19 virus infections around the world. People are afraid of it, thinking that it would appear as a pandemic like COVID-19. As such, it is crucial to detect them earlier before widespread community transmission. AI-based detection could help identify them at the early stage. In this paper, we aim to compare 13 different pre-trained deep learning (DL) models for the Monkeypox virus detection. For this, we initially fine-tune them with the addition of universal custom layers for all of them and analyse the results using four well-established measures: Precision, Recall, F1-score, and Accuracy. After the identification of the best-performing DL models, we ensemble them to improve the overall performance using a majority voting over the probabilistic outputs obtained from them. We perform our experiments on a publicly available dataset, which results in average Precision, Recall, F1-score, and Accuracy of 85.44%, 85.47%, 85.40%, and 87.13%, respectively with the help of our proposed ensemble approach. These encouraging results, which outperform the state-of-the-art methods, suggest that the proposed approach is applicable to health practi- tioners for mass screening.
EXISTING SYSTEM :
From existing research works on virus-related disease detection using DL methods, we observe that the majority of them have employed the transfer learning approach [8, 14] using well-established pre-trained DL methods. Since there are not many works available on Monkeypox virus detection except the work by Ahsan et al.[2]. Their proposal has provided encouraging results in this domain. However, it has three main limitations. First, their models only deal with binary classification with limited performance. Second, they only consider the VGG-16 DL model for transfer learning, which lacks identifying the best- performing pre-trained DL methods and their best combinations to attain optimal performance. Third, their models have insufficient interpretability. As a result, it is difficult to establish trustworthiness among health practitioners during mass screening. To address the aforementioned limitations, we, first, resort to the 13 pre- trained DL models and fine-tune them with the same approach. Second, we evaluate the performance of each DL model using averaged Precision, Recall, F1-score and Accuracy over 5 folds. Third, we ensemble the best-performing models to improve the overall performance.
EXISTING SYSTEM DISADVANTAGES:
1.LESS ACCURACY
2. LOW EFFICIENCY
PROPOSED SYSTEM :
he very deep convolutional neural network such as VGG-16 and VGG-19 produced promising results in a large-scale image classification task. However, it is very hard to train a very deep neural network due to a vanishing gradient problem, i.e., the multiplication of small gradient propagated back to the previous layer start vanishing after a certain depth. The researchers aimed to address the vanishing gradient problem by introducing the concept of skip connection, which allows skipping some layers in the network. The group of layers in the network that use such skip connection are known as residual blocks (Res-Blocks), which are the core of Res Net architecture [9]. Here, we utilise two Res Net architectures: ResNet-50 and ResNet-101. The ResNet-50 consists of 48 Convolution layers, 1 Max-pooling, and 1 Average pooling layer, whereas ResNet-101 includes 99 Convolution layers, 1 Max-pooling and 1 Average pooling layer.
PROPOSED SYSTEM ADVANTAGES:
1.HIGH ACCURACY
2.HIGH EFFICIENCY
SYSTEM REQUIREMENTS
SOFTWARE REQUIREMENTS:
• Programming Language : Python
• Font End Technologies : TKInter/Web(HTML,CSS,JS)
• IDE : Jupyter/Spyder/VS Code
• Operating System : Windows 08/10
HARDWARE REQUIREMENTS:
Processor : Core I3
RAM Capacity : 2 GB
Hard Disk : 250 GB
Monitor : 15″ Color
Mouse : 2 or 3 Button Mouse
Key Board : Windows 08/10
