A lightweight hybrid model for the automatic recognition of uterine fibroid ultrasound images based on deep learning

PURPOSE

Uterine fibroids (UF) are the most frequent tumors in ladies and can pose an enormous threat to complications, such as miscarriage. The accuracy of prognosis may also be affected by way of doctor inexperience and fatigue, underscoring the want for automatic classification fashions that can analyze UF from a giant wide variety of images.

METHODS

A hybrid model has been proposed that combines the MobileNetV2 community and deep convolutional generative adversarial networks (DCGAN) into useful resources for medical practitioners in figuring out UF and evaluating its characteristics. Real-time automated classification of UF can aid in diagnosing the circumstance and minimizing subjective errors. The DCGAN science is utilized for superior statistics augmentation to create first-rate UF images, which are labeled into UF and non-uterine-fibroid (NUF) classes. The MobileNetV2 model then precisely classifies the photos based totally on this data.

RESULTS

The overall performance of the hybrid model contrasts with different models. The hybrid model achieves a real-time classification velocity of 40 frames per second (FPS), an accuracy of 97.45%, and an F1 rating of 0.9741.

CONCLUSION

By using this deep learning hybrid approach, we address the shortcomings of the current classification methods of uterine fibroid.

Synthesis of sEMG Signals for Hand Gestures Using a 1DDCGAN

The emergence of modern prosthetics controlled by bio-signals has been facilitated by AI and microchip technology innovations. AI algorithms are trained using sEMG produced by muscles during contractions. The data acquisition procedure may result in discomfort and fatigue, particularly for amputees. Furthermore, prosthetic companies restrict sEMG signal exchange, limiting data-driven research and reproducibility. GANs present a viable solution to the aforementioned concerns. GANs can generate high-quality sEMG, which can be utilised for data augmentation, decrease the training time required by prosthetic users, enhance classification accuracy and ensure research reproducibility. This research proposes the utilisation of a one-dimensional deep convolutional GAN (1DDCGAN) to generate the sEMG of hand gestures. This approach involves the incorporation of dynamic time wrapping, fast Fourier transform and wavelets as discriminator inputs. Two datasets were utilised to validate the methodology, where five windows and increments were utilised to extract features to evaluate the synthesised sEMG quality. In addition to the traditional classification and augmentation metrics, two novel metrics-the Mantel test and the classifier two-sample test-were used for evaluation. The 1DDCGAN preserved the inter-feature correlations and generated high-quality signals, which resembled the original data. Additionally, the classification accuracy improved by an average of 1.21-5%.

A Fault Diagnosis Strategy for Analog Circuits with Limited Samples Based on the Combination of the Transformer and Generative Models

As a pivotal integral component within electronic systems, analog circuits are of paramount importance for the timely detection and precise diagnosis of their faults. However, the objective reality of limited fault samples in operational devices with analog circuitry poses challenges to the direct applicability of existing diagnostic methods. This study proposes an innovative approach for fault diagnosis in analog circuits by integrating deep convolutional generative adversarial networks (DCGANs) with the Transformer architecture, addressing the problem of insufficient fault samples affecting diagnostic performance. Firstly, the employment of the continuous wavelet transform in combination with Morlet wavelet basis functions serves as a means to derive time-frequency images, enhancing fault feature recognition while converting time-domain signals into time-frequency representations. Furthermore, the augmentation of datasets utilizing deep convolutional GANs is employed to generate synthetic time-frequency signals from existing fault data. The Transformer-based fault diagnosis model was trained using a mixture of original signals and generated signals, and the model was subsequently tested. Through experiments involving single and multiple fault scenarios in three simulated circuits, a comparative analysis of the proposed approach was conducted with a number of established benchmark methods, and its effectiveness in various scenarios was evaluated. In addition, the ability of the proposed fault diagnosis technique was investigated in the presence of limited fault data samples. The outcome reveals that the proposed diagnostic method exhibits a consistently high overall accuracy of over 96% in diverse test scenarios. Moreover, it delivers satisfactory performance even when real sample sizes are as small as 150 instances in various fault categories.

Waste Detection System Based on Data Augmentation and YOLO_EC

The problem of waste classification has been a major concern for both the government and society, and whether waste can be effectively classified will affect the sustainable development of human society. To perform fast and efficient detection of waste targets in the sorting process, this paper proposes a data augmentation + YOLO_EC waste detection system. First of all, because of the current shortage of multi-objective waste classification datasets, the heavy workload of human data collection, and the limited improvement of data features by traditional data augmentation methods, DCGAN (deep convolution generative adversarial networks) was optimized by improving the loss function, and an image-generation model was established to realize the generation of multi-objective waste images; secondly, with YOLOv4 (You Only Look Once version 4) as the basic model, EfficientNet is used as the backbone feature extraction network to realize the light weight of the algorithm, and at the same time, the CA (coordinate attention) attention mechanism is introduced to reconstruct the MBConv module to filter out high-quality information and enhance the feature extraction ability of the model. Experimental results show that on the HPU_WASTE dataset, the proposed model outperforms other models in both data augmentation and waste detection.

Geometry Sampling-Based Adaption to DCGAN for 3D Face Generation

Despite progress in the past decades, 3D shape acquisition techniques are still a threshold for various 3D face-based applications and have therefore attracted extensive research. Moreover, advanced 2D data generation models based on deep networks may not be directly applicable to 3D objects because of the different dimensionality of 2D and 3D data. In this work, we propose two novel sampling methods to represent 3D faces as matrix-like structured data that can better fit deep networks, namely (1) a geometric sampling method for the structured representation of 3D faces based on the intersection of iso-geodesic curves and radial curves, and (2) a depth-like map sampling method using the average depth of grid cells on the front surface. The above sampling methods can bridge the gap between unstructured 3D face models and powerful deep networks for an unsupervised generative 3D face model. In particular, the above approaches can obtain the structured representation of 3D faces, which enables us to adapt the 3D faces to the Deep Convolution Generative Adversarial Network (DCGAN) for 3D face generation to obtain better 3D faces with different expressions. We demonstrated the effectiveness of our generative model by producing a large variety of 3D faces with different expressions using the two novel down-sampling methods mentioned above.

Improved Semantic Image Inpainting Method with Deep Convolution Generative Adversarial Networks

With the development of generative adversarial networks (GANs), more and more researchers apply them to image inpainting technologies. However, many existing approaches caused some inpainting images to be unclear or even restore failures due to a failure to keep the consistency of the inpainted content and structures in line with the surroundings. In this article, we propose the Improved Semantic Image Inpainting Method with Deep Convolution GANs, which can resolve this inconsistency. In the proposed method, we design a patch discriminator and contextual loss to jointly perform the accuracy and effectiveness for image inpainting. In addition, we also designed a consistency loss based on deep convolutional neural networks to constrain the difference between the generated image and the original image in the feature space. Our proposed method improves the details and authenticity effectively for the inpainting images. We evaluate our proposed method on two different datasets, and the result shows that our proposed method achieves state-of-the-art results.

Staging study of single-channel sleep EEG signals based on data augmentation

Introduction

Accurate sleep staging is an essential basis for sleep quality assessment and plays an important role in sleep quality research. However, the occupancy of different sleep stages is unbalanced throughout the sleep process, which makes the EEG datasets of different sleep stages have a class imbalance, which will eventually affect the automatic assessment of sleep stages.

Method

In this paper, we propose a Residual Dense Block and Deep Convolutional Generative Adversarial Network (RDB-DCGAN) data augmentation model based on the DCGAN and RDB, which takes two-dimensional continuous wavelet time-frequency maps as input, expands the minority class of sleep EEG data and later performs sleep staging by Convolutional Neural Network (CNN).

Results and discussion

The results of the CNN classification comparison test with the publicly available dataset Sleep-EDF show that the overall sleep staging accuracy of each stage after data augmentation is improved by 6%, especially the N1 stage, which has low classification accuracy due to less original data, also has a significant improvement of 19%. It is fully verified that data augmentation by improving the DCGAN model can effectively improve the classification problem of the class imbalance sleep dataset.

Generative Adversarial Network Image Synthesis Method for Skin Lesion Generation and Classification

Background:

One of the common limitations in the treatment of cancer is in the early detection of this disease. The customary medical practice of cancer examination is a visual examination by the dermatologist followed by an invasive biopsy. Nonetheless, this symptomatic approach is timeconsuming and prone to human errors. An automated machine learning model is essential to capacitate fast diagnoses and early treatment.

Objective:

The key objective of this study is to establish a fully automatic model that helps Dermatologists in skin cancer handling process in a way that could improve skin lesion classification accuracy.

Method:

The work is conducted following an implementation of a Deep Convolutional Generative Adversarial Network (DCGAN) using the Python-based deep learning library Keras. We incorporated effective image filtering and enhancement algorithms such as bilateral filter to enhance feature detection and extraction during training. The Deep Convolutional Generative Adversarial Network (DCGAN) needed slightly more fine-tuning to ripe a better return. Hyperparameter optimization was utilized for selecting the best-performed hyperparameter combinations and several network hyperparameters. In this work, we decreased the learning rate from the default 0.001 to 0.0002, and the momentum for Adam optimization algorithm from 0.9 to 0.5, in trying to reduce the instability issues related to GAN models and at each iteration the weights of the discriminative and generative network were updated to balance the loss between them. We endeavour to address a binary classification which predicts two classes present in our dataset, namely benign and malignant. More so, some wellknown metrics such as the receiver operating characteristic -area under the curve and confusion matrix were incorporated for evaluating the results and classification accuracy.

Results:

The model generated very conceivable lesions during the early stages of the experiment and we could easily visualise a smooth transition in resolution along the way. Thus, we have achieved an overall test accuracy of 93.5% after fine-tuning most parameters of our network.

Conclusion:

This classification model provides spatial intelligence that could be useful in the future for cancer risk prediction. Unfortunately, it is difficult to generate high quality images that are much like the synthetic real samples and to compare different classification methods given the fact that some methods use non-public datasets for training.

A Bi-fold Approach to Detect and Classify COVID-19 X-Ray Images and Symptom Auditor

In this paper, we propose an ensemble-based transfer learning method to predict the X-ray image of a COVID-19 affected person. We have used a weighted Euclidean distance average as the parameter to ensemble the transfer learning model viz. ResNet50, VGG16, VGG19, Xception, and InceptionV3. Image augmentations have been carried out using generative adversarial network modelling. We took 784 training images, and 278 test images to validate our model accuracy, and the accuracy of our proposed model was around 98.67% for the training data set and 95.52% for the test data set. Along with that, we also propose a genetic algorithm optimized classification algorithm, to analyze the symptoms of COVID-19 for low, medium, and high-risk patients. The accuracy for the optimized set overshadowed the accuracy of un-optimized classification, and the optimized accuracy is as high as 88.96% for the optimized model. The novelty of this paper lies in the bi-sided model of the paper, i.e., we propose two major models, and one is the genetic algorithm optimized model to analyze the symptoms for a patient of varied risk and the other is to classify the X-ray image using an ensemble-based transfer learning model.

Data Augmentation for Motor Imagery Signal Classification Based on a Hybrid Neural Network

As an important paradigm of spontaneous brain-computer interfaces (BCIs), motor imagery (MI) has been widely used in the fields of neurological rehabilitation and robot control. Recently, researchers have proposed various methods for feature extraction and classification based on MI signals. The decoding model based on deep neural networks (DNNs) has attracted significant attention in the field of MI signal processing. Due to the strict requirements for subjects and experimental environments, it is difficult to collect large-scale and high-quality electroencephalogram (EEG) data. However, the performance of a deep learning model depends directly on the size of the datasets. Therefore, the decoding of MI-EEG signals based on a DNN has proven highly challenging in practice. Based on this, we investigated the performance of different data augmentation (DA) methods for the classification of MI data using a DNN. First, we transformed the time series signals into spectrogram images using a short-time Fourier transform (STFT). Then, we evaluated and compared the performance of different DA methods for this spectrogram data. Next, we developed a convolutional neural network (CNN) to classify the MI signals and compared the classification performance of after DA. The Fréchet inception distance (FID) was used to evaluate the quality of the generated data (GD) and the classification accuracy, and mean kappa values were used to explore the best CNN-DA method. In addition, analysis of variance (ANOVA) and paired t-tests were used to assess the significance of the results. The results showed that the deep convolutional generative adversarial network (DCGAN) provided better augmentation performance than traditional DA methods: geometric transformation (GT), autoencoder (AE), and variational autoencoder (VAE) (p < 0.01). Public datasets of the BCI competition IV (datasets 1 and 2b) were used to verify the classification performance. Improvements in the classification accuracies of 17% and 21% (p < 0.01) were observed after DA for the two datasets. In addition, the hybrid network CNN-DCGAN outperformed the other classification methods, with average kappa values of 0.564 and 0.677 for the two datasets.

Parkinson's Disease EMG Data Augmentation and Simulation with DCGANs and Style Transfer

This paper proposes two new data augmentation approaches based on Deep Convolutional Generative Adversarial Networks (DCGANs) and Style Transfer for augmenting Parkinson’s Disease (PD) electromyography (EMG) signals. The experimental results indicate that the proposed models can adapt to different frequencies and amplitudes of tremor, simulating each patient’s tremor patterns and extending them to different sets of movement protocols. Therefore, one could use these models for extending the existing patient dataset and generating tremor simulations for validating treatment approaches on different movement scenarios.

Lesion Image Synthesis Using DCGANs for Metastatic Liver Cancer Detection

This chapter proposes a method to detect metastatic liver cancer from X-ray CT images using a convolutional neural network (CNN). The proposed method generates various lesion images by the combination of three kinds of generation methods: (1) synthesis using Poisson Blending, (2) generation based on CT value distributions, and (3) generation using deep convolutional generative adversarial networks (DCGANs). The proposed method constructs two kinds of detectors by using synthetic (fake) lesion images generated by the methods as well as real ones. One of the detectors is a 2D CNN for detecting candidate regions in a CT image, and the other is a 3D CNN for validating the candidate regions. Experimental results showed that the proposed method gave 0.30 improvement from 0.65 to 0.95 in terms of the detection rate, and 0.70 improvement from 0.90 to 0.20 in terms of the number of false detections per case. From the results, we confirmed the effectiveness of the proposed method.

Generative Adversarial Networks for the Creation of Realistic Artificial Brain Magnetic Resonance Images

Even as medical data sets become more publicly accessible, most are restricted to specific medical conditions. Thus, data collection for machine learning approaches remains challenging, and synthetic data augmentation, such as generative adversarial networks (GAN), may overcome this hurdle. In the present quality control study, deep convolutional GAN (DCGAN)–based human brain magnetic resonance (MR) images were validated by blinded radiologists. In total, 96 T1-weighted brain images from 30 healthy individuals and 33 patients with cerebrovascular accident were included. A training data set was generated from the T1-weighted images and DCGAN was applied to generate additional artificial brain images. The likelihood that images were DCGAN-created versus acquired was evaluated by 5 radiologists (2 neuroradiologists [NRs], vs 3 non-neuroradiologists [NNRs]) in a binary fashion to identify real vs created images. Images were selected randomly from the data set (variation of created images, 40%–60%). None of the investigated images was rated as unknown. Of the created images, the NRs rated 45% and 71% as real magnetic resonance imaging images (NNRs, 24%, 40%, and 44%). In contradistinction, 44% and 70% of the real images were rated as generated images by NRs (NNRs, 10%, 17%, and 27%). The accuracy for the NRs was 0.55 and 0.30 (NNRs, 0.83, 0.72, and 0.64). DCGAN-created brain MR images are similar enough to acquired MR images so as to be indistinguishable in some cases. Such an artificial intelligence algorithm may contribute to synthetic data augmentation for “data-hungry” technologies, such as supervised machine learning approaches, in various clinical applications.