A Robust Ensemble of Convolutional Neural Networks for the Detection of Monkeypox Disease from Skin Images

Abstract

Monkeypox is a smallpox-like disease that was declared a global health emergency in July 2022. Because of this resemblance, it is not easy to distinguish a monkeypox rash from other similar diseases; however, due to the novelty of this disease, there are no widely used databases for this purpose with which to develop image-based classification algorithms. Therefore, three significant contributions are proposed in this work: first, the development of a publicly available dataset of monkeypox images; second, the development of a classification system based on convolutional neural networks in order to automatically distinguish monkeypox marks from those produced by other diseases; and, finally, the use of explainable AI tools for ensemble networks. For point 1, free images of monkeypox cases and other diseases have been searched in government databases and processed until we are left with only a section of the skin of the patients in each case. For point 2, various pre-trained models were used as classifiers and, in the second instance, combinations of these were used to form ensembles. And, for point 3, this is the first documented time that an explainable AI technique (like GradCAM) is applied to the results of ensemble networks. Among all the tests, the accuracy reaches 93% in the case of single pre-trained networks, and up to 98% using an ensemble of three networks (ResNet50, EfficientNetB0, and MobileNetV2). Comparing these results with previous work, a substantial improvement in classification accuracy is observed.

Developments in Image Processing Using Deep Learning and Reinforcement Learning

“…Some researchers have turned their attention to improving the understanding of the many models. Increased focus has been placed on the way the weights of a neural network can sometimes be difficult to decipher and extract useful information from, which can lead to wrong assumptions and decisions [62]. In order to facilitate communication and discussion, some authors have also attempted to provide a categorization system of DL methodologies based on their applications [31].…”

“…These results validate an upward trend in attention to DL methods, as also described in the previous section. A lot of recent literature, especially in the medical field, has attempted to address the biggest challenges, mainly derived from data scarcity and model performance [14,[61][62][63][64]. Some research has focused on improving perforce or reducing the computational requirements in models such as CNNs [60,65,66] using techniques such as model pruning or compression.…”

“…One clear advantage of these approaches is that some exams and analyses are based on a human inspection, which can be time-consuming, require extensive training for the personnel, and may also be subject to subjectivity and variability in the observers [16,103,104]. In 2023, Luis et al applied explainable artificial intelligence (xAI) as a way to test the application of different classifiers for monkeypox detection and to better understand the results [62]. With a greater focus on properly interpreting the model results, approaches such as these are increasingly more common.…”

Ensemble Transfer Learning for Hand-sign Digit Image Classification

“…Beyond transfer learning, we also explore the potential of ensemble methods, a popular machine learning approach that combines multiple prediction models to achieve superior predictive solutions compared to individual models. Ensemble methods have proven valuable in a range of machine learning tasks, including image classification [19,20].…”

Diagnosis Aid System for Colorectal Cancer Using Low Computational Cost Deep Learning Architectures

“…Not surprisingly, in recent years, there has been a proliferation in the use of Deep Learning techniques to design systems to aid diagnosis with medical images. Thus, we can find works analyzing skin images [7], various types of cancers [8], glaucoma [9,10], or even X-ray images [11,12], all of them using Deep Learning approaches with classification results above 90%. In addition, in recent years, several studies have applied the principles of AI to the detection of colorectal cancer using medical imaging.…”