Deep learning requires a large amount of data to perform well. However, the field of medical image analysis suffers from a lack of sufficient data for training deep learning models. Moreover, medical images require manual labeling, usually provided by human annotators coming from various backgrounds. More importantly, the annotation process is time-consuming, expensive, and prone to errors. Transfer learning was introduced to reduce the need for the annotation process by transferring the deep learning models with knowledge from a previous task and then by fine-tuning them on a relatively small dataset of the current task. Most of the methods of medical image classification employ transfer learning from pretrained models, e.g., ImageNet, which has been proven to be ineffective. This is due to the mismatch in learned features between the natural image, e.g., ImageNet, and medical images. Additionally, it results in the utilization of deeply elaborated models. In this paper, we propose a novel transfer learning approach to overcome the previous drawbacks by means of training the deep learning model on large unlabeled medical image datasets and by next transferring the knowledge to train the deep learning model on the small amount of labeled medical images. Additionally, we propose a new deep convolutional neural network (DCNN) model that combines recent advancements in the field. We conducted several experiments on two challenging medical imaging scenarios dealing with skin and breast cancer classification tasks. According to the reported results, it has been empirically proven that the proposed approach can significantly improve the performance of both classification scenarios. In terms of skin cancer, the proposed model achieved an F1-score value of 89.09% when trained from scratch and 98.53% with the proposed approach. Secondly, it achieved an accuracy value of 85.29% and 97.51%, respectively, when trained from scratch and using the proposed approach in the case of the breast cancer scenario. Finally, we concluded that our method can possibly be applied to many medical imaging problems in which a substantial amount of unlabeled image data is available and the labeled image data is limited. Moreover, it can be utilized to improve the performance of medical imaging tasks in the same domain. To do so, we used the pretrained skin cancer model to train on feet skin to classify them into two classes-either normal or abnormal (diabetic foot ulcer (DFU)). It achieved an F1-score value of 86.0% when trained from scratch, 96.25% using transfer learning, and 99.25% using double-transfer learning.

https://www.mdpi.com/2072-6694/13/7/1590/pdf

Novel Transfer Learning Approach for Medical Imaging with Limited Labeled Data.

Lung cancer is one of the most common diseases among humans and one of the major causes of growing mortality. Medical experts believe that diagnosing lung cancer in the early phase can reduce death with the illustration of lung nodule through computed tomography (CT) screening. Examining the vast amount of CT images can reduce the risk. However, the CT scan images incorporate a tremendous amount of information about nodules, and with an increasing number of images make their accurate assessment very challenging tasks for radiologists. Recently, various methods are evolved based on handcraft and learned approach to assist radiologists. In this paper, we reviewed different promising approaches developed in the computer-aided diagnosis (CAD) system to detect and classify the nodule through the analysis of CT images to provide radiologists' assistance and present the comprehensive analysis of different methods.

Lung cancer identification: a review on detection and classification.

Lung Nodule Detection based on Faster R-CNN Framework

The development of printed circuit boards (PCBs) has so far followed a traditional linear economy value chain, leading to high volumes of waste production and loss of value at the end-of-life. Consequentially, the electronics industry requires a transition to more sustainable practices. This review article presents an overview of the potential solutions and new opportunities that may arise from the greater use of emerging sustainable materials and resource-efficient manufacturing. A brief contextual summary about how the international management of waste PCBs (WPCBs) and legalization have evolved over the past 20 years is presented along with a review of the existing materials used in PCBs. The environmental and human health assessments of these materials relative to their usage with PCBs are also explained. This enables the identification of which ecofriendly materials and new technologies will be needed to improve the sustainability of the industry. Following this, a comprehensive analysis of existing WPCB processing is presented. Finally, a detailed review of potential solutions is provided, which has been partitioned by the use of emerging sustainable materials and resource-efficient manufacturing. It is hoped that this discussion will transform existing manufacturing facilities and inform policies, which currently focus on waste management, toward waste reduction and zero waste.

Electronic Waste Reduction Through Devices and Printed Circuit Boards Designed for Circularity

For this paper, the problem of energy/voltage management in photovoltaic (PV)/battery systems was studied, and a new fractional-order control system on basis of type-3 (T3) fuzzy logic systems (FLSs) was developed. New fractional-order learning rules are derived for tuning of T3-FLSs such that the stability is ensured. In addition, using fractional-order calculus, the robustness was studied versus dynamic uncertainties, perturbation of irradiation, and temperature and abruptly faults in output loads, and, subsequently, new compensators were proposed. In several examinations under difficult operation conditions, such as random temperature, variable irradiation, and abrupt changes in output load, the capability of the schemed controller was verified. In addition, in comparison with other methods, such as proportional-derivative-integral (PID), sliding mode controller (SMC), passivity-based control systems (PBC), and linear quadratic regulator (LQR), the superiority of the suggested method was demonstrated.

https://www.mdpi.com/2079-9292/9/9/1455/pdf

Fractional-Order Fuzzy Control Approach for Photovoltaic/Battery Systems under Unknown Dynamics, Variable Irradiation and Temperature

Lung cancer is one of the common causes of cancer deaths. Early detection and treatment of lung cancer is essential. However, the detection of lung cancer in patients produces many false positives. Therefore, increasing the accuracy of the classification of diagnosis or true detection by computed tomography (CT) is a difficult task. Solving this problem using intelligent and automated methods has become a hot research topic in recent years. Hence, we propose a 2D convolutional neural network (2D CNN) with Taguchi parametric optimization for automatically recognizing lung cancer from CT images. In the Taguchi method, 36 experiments and 8 control factors of mixed levels were selected to determine the optimum parameters of the 2D CNN architecture and improve the classification accuracy of lung cancer. The experimental results show that the average classification accuracy of the 2D CNN with Taguchi parameter optimization and the original 2D CNN in lung cancer recognition are 91.97% and 98.83% on the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) dataset, and 94.68% and 99.97% on the International Society for Optics and Photonics with the support of the American Association of Physicists in Medicine (SPIE-AAPM) dataset, respectively. The proposed method is 6.86% and 5.29% more accurate than the original 2D CNN on the two datasets, respectively, proving the superiority of proposed model.

Using 2D CNN with Taguchi Parametric Optimization for Lung Cancer Recognition from CT Images

The rapid development of e-commerce has resulted in optimization of the industrial structure of Chinese enterprises and has improved the Chinese economy. E-commerce transaction volume is an evaluation index used to determine the development level of e-commerce. This study proposed a model for forecasting e-commerce transaction volume. First, a hybrid moth–flame optimization algorithm (HMFO) was proposed. The convergence ability of the HMFO algorithm was analyzed on the basis of test functions. Second, using data provided by the China Internet Network Information Center, factors influencing e-commerce transaction volume were analyzed. The input variables of the e-commerce transaction volume prediction model were selected by analyzing correlation coefficients. Finally, a hybrid extreme learning machine and hybrid-strategy-based HMFO (ELM-HMFO) method was proposed to predict the volume of e-commerce transactions. The prediction results revealed that the root mean square error of the proposed ELM-HMFO model was smaller than 0.5, and the determination coefficient was 0.99, which indicated that the forecast e-commerce transaction volume was satisfactory. The proposed ELM-HMFO model can promote the industrial upgrading and development of e-commerce in China.

Forecasting of e-commerce transaction volume using a hybrid of extreme learning machine and improved moth-flame optimization algorithm

In general, a convolutional neural network (CNN) consists of one or more convolutional layers, pooling layers, and fully connected layers. Most designers adopt a trial-and-error method to select CNN parameters. In this study, an AlexNet network with optimized parameters is proposed for face image recognition. A Taguchi method is used for selecting preliminary factors and experiments are performed through orthogonal table design. The proposed method filters out factors that are significantly affected. Finally, experimental results show that the proposed Taguchi-based AlexNet network obtains 87.056% and 98.72% average accuracy of image gender recognition in the CIA and MORPH databases, respectively. In addition, the average accuracy of the proposed Taguchi-based AlexNet network is 1.576% and 3.47% higher than that of the original AlexNet network in CIA and MORPH databases, respectively.

https://www.mdpi.com/2079-9292/9/8/1227/pdf

Using Convolutional Neural Networks Based on a Taguchi Method for Face Gender Recognition

In recent years, convolutional neural networks (CNNs) have been successfully used in image recognition and image classification. General CNNs only use a single image as feature extraction. If the quality of the obtained image is not good, it is easy to cause misjudgment or recognition error. Therefore, this study proposes the feature fusion of a dual-input CNN for the application of face gender classification. In order to improve the traditional feature fusion method, this paper also proposes a new feature fusion method, called the weighting fusion method, which can effectively improve the overall accuracy. In addition, in order to avoid the parameters of the traditional CNN being determined by the user, this paper uses a uniform experimental design (UED) instead of the user to set the network parameters. The experimental results show that in the dual-input CNN experiment, average accuracy rates of 99.98% and 99.11% on the CIA and MORPH data sets are achieved, respectively, which is superior to the traditional feature fusion method.

https://www.mdpi.com/2076-3417/10/9/3166/pdf

Using Feature Fusion and Parameter Optimization of Dual-input Convolutional Neural Network for Face Gender Recognition

Breast cancer, a common cancer type, is a major health concern in women. Recently, researchers used convolutional neural networks (CNNs) for medical image analysis and demonstrated classification performance for breast cancer diagnosis from within histopathological image datasets. However, the parameter settings of a CNN model are complicated, and using Breast Cancer Histopathological Database data for the classification is time-consuming. To overcome these problems, this study used a uniform experimental design (UED) and optimized the CNN parameters of breast cancer histopathological image classification. In UED, regression analysis was used to optimize the parameters. The experimental results indicated that the proposed method with UED parameter optimization provided 84.41% classification accuracy rate. In conclusion, the proposed method can improve the classification accuracy effectively, with results superior to those of other similar methods.

https://www.mdpi.com/2075-4418/10/9/662/pdf

Cheng-Jian Lin

Papers

Using 2D CNN with Taguchi Parametric Optimization for Lung Cancer Recognition from CT Images

Forecasting of e-commerce transaction volume using a hybrid of extreme learning machine and improved moth-flame optimization algorithm

Using Convolutional Neural Networks Based on a Taguchi Method for Face Gender Recognition

Using Feature Fusion and Parameter Optimization of Dual-input Convolutional Neural Network for Face Gender Recognition

Optimization of Deep Learning Network Parameters Using Uniform Experimental Design for Breast Cancer Histopathological Image Classification.