WiDet: Wi-Fi based device-free passive person detection with deep convolutional neural networks

Recommending music based on a user’s music preference is a way to improve user listening experience. Finding the correlation between the user data (e.g., location, time of the day, music listening history, emotion, etc.) and the music is a challenging task. In this paper, we propose an emotion-aware personalized music recommendation system (EPMRS) to extract the correlation between the user data and the music. To achieve this correlation, we combine the outputs of two approaches: the deep convolutional neural networks (DCNN) approach and the weighted feature extraction (WFE) approach. The DCNN approach is used to extract the latent features from music data (e.g., audio signals and corresponding metadata) for classification. In the WFE approach, we generate the implicit user rating for music to extract the correlation between the user data and the music data. In the WFE approach, we use the term-frequency and inverse document frequency (TF-IDF) approach to generate the implicit user ratings for the music. Later, the EPMRS recommends songs to the user based on calculated implicit user rating for the music. We use the million songs dataset (MSD) to train the EPMRS. For performance comparison, we take the content similarity music recommendation system (CSMRS) as well as the personalized music recommendation system based on electroencephalography feedback (PMRSE) as the baseline systems. Experimental results show that the EPMRS produces better accuracy of music recommendations than the CSMRS and the PMRSE. Moreover, we build the Android and iOS APPs to get realistic data of user experience on the EPMRS. The collected feedback from anonymous users also show that the EPMRS sufficiently reflect their preference on music.

The feasibility of localizing, segmenting, and classifying individual cells in multi-channel confocal microscopy images is highly dependent on image quality. In certain applications, with good image quality, the segmentation can be trivial and accomplished via thresholding, watershed, or a collection of other well-established and studied heuristics; however, at the limit of poor image quality and complex image features, these techniques fail. It is at this limit that deep convolutional neural network (DCNN) approaches excel. Our research studies the interaction of individual immune cells and their shape changes relative to inflammatory immune reactions1 using multi-channel immunofluorescence imaging of renal biopsies from patients with inflammatory kidney disease. We present here a deep learning methodology for application to nuclear and cell membrane immunofluorescent stains to automatically segment and classify multiple T-cell and dendritic cell types. With both T-cells and dendritic cells segmented, we are able to study how T-cells with different surface antigens change shape with proximity to dendritic cells. Shape changes are seen when T-cells move close to dendritic cells and interact. We use a sliding window, max-filtering DCNN to segment and classify 3 cell types from 6 image stains channels within a single DCNN. This DCNN maintains images at original resolution throughout the network using dilated convolutions and max-filtering in place of max pooling layers. In addition, we use 3D convolution kernels with two spatial dimensions and one channel dimension. This allows us to output a multi-class binary classification based on multichannel data at the original image resolution. We trained and validated the network across 24 patients with 8,572 segmented cells. Our results demonstrate a mean Dice-Sorensen score of 0.78 +/- 0.18, a mean classification sensitivity of 0.76, and a mean classification specificity of 0.75 across all 3 segmented cell types.

In this paper, we propose a deep learning convolutional neural network (CNN) approach to classify arrhythmia based on the time interval of the QRS complex of the ECG signal. The ECG signal was denoised using multiple filters based on the Pan Tompkins algorithm. QRS detection has been done using Pan Tompkins Algorithm. Then the QRS complex is identified using local peaks based technique inside the layers of the Convolutional Neural Network where the repeated application of the same filter to our input results in a map of activations called a feature map, indicating the locations and strength of a detected feature in an input which in our case is the changes in the q-s time interval. Based on the R-R time interval, Heart rate variability (HRV) was computed, and Poincare plot was generated. Instead of using raw ECG signal to train the CNN, we used the feature extracted from ECG signal obtained from Physionet database to train the CNN and map the pattern changes for different classes of diseases. The classifier was then used to classify the test input as either or normal, tachyarrhythmia or intracardiac atrial fibrillation. Data acquisition, ECG data pre-processing and CNN classifier are the several methods that are involved for the classification of several arrhythmias.

Skin cancer is one of the most dangerous types of cancer due to its immediate appearance and the possibility of rapid spread. It arises from uncontrollably growing cells, rapidly dividing cells in one area of the body, invading other bodily tissues, and spreading throughout the body. Early detection helps prevent cancer progress from reaching critical levels, reducing the risk of complications and the need for more aggressive treatment options. Convolutional neural networks (CNNs) revolutionize skin cancer diagnosis by extracting intricate features from images, enabling an accurate classification of lesions. Their role extends to early detection, providing a powerful tool for dermatologists to identify abnormalities in their nascent stages, ultimately improving patient outcomes. This study proposes a novel deep convolutional neural network (DCNN) approach to classifying skin cancer lesions. The proposed DCNN model is evaluated using two unbalanced datasets, namely HAM10000 and ISIC-2019. The DCNN model is compared with other transfer learning models, including VGG16, VGG19, DenseNet121, DenseNet201, and MobileNetV2. Its performance is assessed using four widely used evaluation metrics: accuracy, recall, precision, F1-score, specificity, and AUC. The experimental results demonstrate that the proposed DCNN model outperforms other deep learning (DL) models that utilized these datasets. The proposed DCNN model achieved the highest accuracy with the HAM10000 and ISIC-2019 datasets, reaching 98.5%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$98.5\\%$$\\end{document} and 97.1%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$97.1\\%$$\\end{document}, respectively. These experimental results show how competitive and successful the DCNN model is in overcoming the problems caused by class imbalance and raising skin cancer classification accuracy. Furthermore, the proposed model demonstrates superior performance, particularly excelling in terms of accuracy, compared to other recent studies that utilize the same datasets, which highlights the robustness and effectiveness of the proposed DCNN.

Detecting major depressive disorder presence using passively-collected wearable movement data in a nationally-representative sample

Although the left ventricle (LV) is commonly assessed in current clinical practice, the assessment of the right ventricle (RV) also plays an important role in the diagnosis of cardiovascular disease. RV failure has numerous causes, including pulmonary hypertension, myocardial infarction, and congenital heart disease. However, assessment of the RV is more challenging than the LV due to its complex shape and thin walls. This study proposes an automated approach to delineate the RV from magnetic resonance imaging (MRI) scans using a deep convolutional neural network approach. The proposed method uses nnU-Net, a self-adapting framework based on the U-Net neural network approach for the segmentation of the RV from short and long axis MRI images at the end-systolic and end-diastolic phases of the heart. The proposed neural network models were trained using the datasets provided by Multi-Disease, Multi-View & Multi-Center Right Ventricular Segmentation in Cardiac MRI Challenge hosted by MICCAI 2021 conference. The quantitative evaluations were performed by the challenge organizers on a test set consisting of MRI scans acquired from 160 patients where the images and ground truth were blinded to the challenge participants. The proposed method yielded an overall Dice metric of \(92.47\%\) with \(92.73\%\) and \(91.71\%\) for short and long axis images, respectively. The corresponding Hausdorff distance values were 9.08 mm, 10.05 mm, and 6.16 mm, respectively.KeywordsCardiac magnetic resonance imagingDeep convolutional neural networksRight ventricleImage segmentation

Diabetic Retinopathy (DR) is eye disease caused through the retinal damage induced through long-term diabetes mellitus illness. In the initial stage, DR has minor vision difficulties or no symptoms but it outputs in vision loss if it is not treated early. However, overfitting occurs when a model learns noise or specific features from a small dataset which leading to poor generalization. To solve this issue, this research proposed an Otsu Multi-level Adaptive Thresholding with Deep Convolution Neural Network (OMAT+DCNN) approach for DR segmentation and classification. The Contrast Limited Adaptive Histogram Equalization (CLAHE) is used for preprocessing input image which enhances the contrast level and image visibility without amplifying noise. The OMAT is used for segmenting the affected regions and ResNet50 is used for extracting Deep Learning (DL) features. Then, relevant features are selected through Multi-Verse Optimizer (MVO) and DCNN is used for classification. The OMAT improves segmentation accuracy by considering multiple thresholds which adopts to local intensity variations within the image, even when the dataset is limited. DCNN capturing intricate patterns and spatial hierarchies within images and its deep architecture allows to learn complex representations which leads to enhance accuracy. The parameters like dice coefficient, Intersection over Union (IoU), Mean IoU (MIoU), precision, accuracy, f1-score and sensitivity are used for assessing performance. The OMAT+DCNN reaches 0.993 of accuracy when compared to existing techniques such as Convolutional Transformer Network (CTNet) and Residual Attention Network (RAN).

In this paper we describe a Deep Convolutional Neural Network (DNN) approach to perform two sentiment detection tasks: message polarity classification and contextual polarity disambiguation.We apply the proposed approach for the SemEval-2014 Task 9: Sentiment Analysis in Twitter.Despite not using any handcrafted feature or sentiment lexicons, our system achieves very competitive results for Twitter data.

Protective measures against small unmanned aerial vehicles (UAVs) are vital from a national security perspective. As a result, the importance of surveillance systems that automatically identify and classify low radar cross section (RCS) aerial targets increases. In this work, an indigenously developed continuous wave (CW) (X-band: 10 GHz) radar is used to build a diversified “DIAT- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula> SAT” dataset comprising 4849 micro-Doppler signature images of five different small aerial targets. We also proposed a transfer learning-based deep convolutional neural network (DCNN) approach for classifying low RCS aerial targets. We demonstrated the classification accuracy of 95% and 97%, with VGG16 and VGG19 as feature extractors, respectively, with minimal false-negative and -positive results. The open-field experimental classes covered in this work are: 1) a two-blade rotor; 2) a three-short-blade rotor; 3) a three-long-blade rotor; 4) a quadcopter; 5) a bionic bird; and 6) a two-blade-rotor and bionic bird. We also observed a good classification accuracy (>97%) when more than one target is operated simultaneously.

Diabetic retinopathy (DR) is one of the primary causes of preventable blindness, highlighting the necessity for accurate and automated retinal screening systems. Manual diagnosis through fundus image inspection is time-consuming and prone to subjective interpretation, particularly in regions with limited access to ophthalmic specialists. This study presents a deep convolutional neural network (CNN) approach based on ResNet50 architecture with fine-tuning for multi-class classification of retinal diseases. The proposed model was developed using the APTOS 2019 Blindness Detection dataset, consisting of 3,662 fundus images categorized into five levels of DR severity. A robust preprocessing pipeline, including illumination correction, contrast enhancement, normalization, and extensive data augmentation, was implemented to improve image quality and balance the dataset. The network was trained using the Adam optimizer with a learning rate of 1×10?? and categorical cross-entropy loss for 30 epochs under an 80:20 train–validation split. Experimental evaluation demonstrated high performance with 92.4% accuracy, 0.91 precision, 0.92 recall, 0.91 F1-score, and an AUC of 0.95, outperforming baseline CNN and VGG16 models. Furthermore, Grad-CAM visualization confirmed that the model accurately localized critical retinal regions associated with microaneurysms, hemorrhages, and exudates, enhancing interpretability and clinical trust. The proposed ResNet50-based framework provides an explainable, efficient, and reliable solution for automated diabetic retinopathy detection, supporting large-scale tele-ophthalmology and early diagnosis applications in medical imaging

Nowadays, the number of patients with brain tumors is steadily increasing, diagnosis and isolation of the tumor play an important role in the process of treatment and surgery. Due to the high error of manual segmentation of the tumor, algorithms that perform this operation with less error are of great importance. Convolutional neural networks have made great progress in the field of medical imaging. The use of imaging techniques and pattern recognition in the diagnosis and automatic determination of brain tumors by MRI imaging reduces errors, human error and speeds up detection. The artificial convolutional neural network (CNN) has been widely used in the diagnosis of intelligent cancers and has significantly reduced the error rate. Therefore, in this paper, we present a new method using a combination of convolutional and multi-scale artificial neural network that has significantly increased the accuracy of tumor diagnosis. This study presents a multidisciplinary convolution neural network (MCNN) approach to classifying tumors that can be used as an important part of automated diagnosis systems for accurate cancer diagnosis. Based on the MCNN structure, which presents the MRI image to several deep convolutional neural networks of varying sizes and resolutions, the stage of extracting classical hand-made features is avoided. This approach proposes better classification rates than the classical methods. This study uses a multi-scale convolution technique to achieve a detection accuracy of 95/4%, which shows the efficiency of the proposed method.

Convolutional neural network (CNN) is a promising image recognition technique for winter road surface condition (RSC), a measure that is crucial for winter maintenance operations. In the past, researchers have designed RSC CNN models that displayed acceptable results but did so focusing solely on obtaining high classification accuracy without any consideration for efficiency. Furthermore, when it comes to model development itself, architecture design requires expertise in CNN as well as rich knowledge in the investigated problem itself. To rectify these issues, this paper proposes an innovative approach to automatically design RSC CNN architecture without compromising classification accuracy. The proposed approach uses a weighted sum method, which provides the freedom of choosing relative importance level between accuracy and efficiency. Once the relative importance has been set, one of the most successful and widely adopted heuristics, namely, simulated annealing (SA), is employed to generate (sub)optimal solutions. Results show that both accuracy and efficiency of the automatically generated CNNs are better or at least comparable to the two selected state-of-the-art CNN models, ResNet50 and MobileNet, achieving as high as 93.44% classification accuracy. Ultimately, the outcome of this study fills the gap in existing CNN design methods that do not consider the tradeoff between accuracy and efficiency while providing insight into the effect varying architectures have on CNN model performance.

We are developing a decision support system for treatment response assessment of bladder cancer in CT urography (CTU). Accurate segmentation of bladder cancer is a critical and challenging task. We previously developed a bladder cancer segmentation method using a deep learning convolutional neural network and level set (DL-CNN+LS) approach. In this study we investigated the application of a U-Net based deep learning (U-Net) model for bladder cancer segmentation. Our new U-Net method did not require the second-stage level set refinement, greatly simplifying the overall segmentation pipeline. The proposed U-Net model utilized a user-defined box to direct the attention of the U-Net to the lesion region by masking out the structured background outside the box. We trained and evaluated the performance of the U-Net segmentations by using hand-drawn 3D contours from a radiologist as reference standard. The segmentation accuracy was evaluated by the average volume intersection ratio (AVI), average percent volume error (AVE), average absolute volume error (AAVE), average minimum distance (AMD), and the Jaccard index (JI). On the validation set, the cropped U-Net achieved values of AVI = 65.5&plusmn;20.2%, AVE = 3.7&plusmn;44.4%, AAVE = 30.9±30.8%, AMD = 2.9&plusmn;1.5 mm, and JI = 51.4&plusmn;16.8%. The previous DL-CNN+LS approach achieved values of AVI = 36.2&plusmn;28.4%, AVE = 51.1&plusmn;39.3%, AAVE = 52.0&plusmn;38.0%, AMD = 4.1&plusmn;1.7 mm, and JI = 32.9&plusmn;27.2%. On the independent test set, the cropped U-Net achieved values of AVI = 66.0&plusmn;22.8%, AVE = -3.7&plusmn;39.4%, AAVE = 28.1&plusmn;27.3%, AMD = 4.5&plusmn;3.3 mm, and JI = 49.2&plusmn;20.0%. The DL-CNN+LS achieved values of AVI = 31.2&plusmn;24.3%, AVE = 59.9&plusmn;30.9%, AAVE = 60.8&plusmn;29.0%, AMD = 5.5&plusmn;2.2 mm, and JI = 27.9&plusmn;20.8%, respectively. The results demonstrated that the U-Net model could achieve a higher accuracy than the previous DL-CNN+LS model while reducing the complexity of the segmentation pipeline.

In modern manufacturing the ability of retracing produced components is crucial for quality management and process optimization. Tracking is essential, especially for analyzing the influence of the production parameters on the final quality of the castings. In the iron foundry industry, common marking methods, such as a datamatrix code, cannot be used due to harsh environmental conditions and the rough surface of the cast parts. This work presents a new coding and reading system that guarantees unique marking in the casting process.The coding is built up over several beveled pins and is read out using an optical 2D handheld scanner. With a deep convolutional neural network approach of object detection and classification, a stable image processing algorithm is presented. With a first prototype a reading accuracy of 99.86% for each pin was achieved with an average scanning time of 0.43 s. The presented code is compatible with existing foundry processes, while the handheld scanner is intuitive and reliable. This allows immediate benefits for process optimization.

Gastric slow wave abnormalities have been associated with gastric motility disorders. Invasive studies in humans have described normal and abnormal propagation of the slow wave. This study aims to disambiguate the abnormally functioning wave from one of normalcy using multi-electrode abdominal waveforms of the electrogastrogram (EGG). Human stomach and abdominal models are extracted from computed tomography scans. Normal and abnormal slow waves are simulated along stomach surfaces. Current dipoles at the stomachs surface are propagated to virtual electrodes on the abdomen with a forward model. We establish a deep convolutional neural network (CNN) framework to classify normal and abnormal slow waves from the multi-electrode waveforms. We investigate the effects of non-idealized measurements on performance, including shifted electrode array positioning, smaller array sizes, high body mass index (BMI), and low signal-to-noise ratio (SNR). We compare the performance of our deep CNN to a linear discriminant classifier using wave propagation spatial features. A deep CNN framework demonstrated robust classification, with accuracy above 90% for all SNR above 0dB, horizontal shifts within 3cm, vertical shifts within 6cm, and abdominal tissue depth within 6cm. The linear discriminant classifier was much more vulnerable to SNR, electrode placement, and BMI. This is the first study to attempt and, moreover, succeed in using a deep CNN to disambiguate normal and abnormal gastric slow wave patterns from high-resolution EGG data. These findings suggest that multi-electrode cutaneous abdominal recordings have the potential to serve as widely deployable clinical screening tools for gastrointestinal foregut disorders.