Optimizing CNN using Fast Fourier Transformation for Object Recognition

Chronic wounds cause a significant burden to affected patients and to society. Effective and objective diagnostic and monitoring methods are needed in wound care, and artificial intelligence offers one promising alternative. In this study, real-world wound images were used to train a convolutional neural network to automatically segment wound area and wound tissues on an image. The study included altogether 362 images of venous, arterial, vasculitis and pyoderma gangrenosum wounds. The model was based on a convolutional neural network architecture U-Net, and fully supervised learning was utilised during the training phase. Wound area reached a Dice Similarity Coefficient (DSC) of 0.927 and Intersection over Union (IoU) of 0.868 using an augmented dataset with pretraining. Fibrinous exudate and granulation performed fairly well with DSC 0.750 and 0.696, and with IoU 0.659 and 0.601, respectively. Necrosis present in only 56 images achieved lower performance with DSC 0.503 and IoU 0.502. In conclusion, this study suggested that it is possible to train a neural network to perform well with images taken for purely clinical purposes. Besides wound area, several wound structures can be identified, but wound structure identification performance is dependent on the number of images featuring the structure.

Most semantic segmentation models based on deep convolutional neural network (CNN) typically require a large number of weight parameters, high hardware resources for storage and computation. Moreover, redesigning a compact network suffers from some training problems, such as under-fitting. A human segmentation algorithm is proposed based on compressed deep CNN to optimize the convolution layers and filters. PSPNet-50 is fine-tuned on the human segmentation dataset to obtain the human segmentation model with higher accuracy. Then the convolutional-layer level pruning and corresponding structure optimization are performed so that the parameters of the model are substantially reduced. Finally, the two-stage global filter-level pruning strategy is used. Compared with the method of layer by layer pruning and retraining, our strategy not only reduces parameters of the model and saves the time of retraining, but also keeps the high IoU (Intersection over Union) accuracy. In addition, by adding auxiliary losses in the network during training CNN, the supervised training of the network is improved, and IoU is further increased. Compared to the model before compression, the sufficient experiments show that the parameter number, computation cost, memory consumption, and parameter storage are decreased by 1/7.5, 5.6/6.6, 0.7/1, 6.5/7.5, respectively, while the segmentation speed is accelerated by 2.4 times, and IoU on test set reaches 93.2%.

This study was aimed to explore the adoption value of magnetic resonance imaging (MRI) under convolutional neural networks (CNN) in the therapeutic effect of nasopharyngeal carcinoma (NPC) radiotherapy. A total of 54 NPC patients were recruited. CNN was employed to perform 3D visualization processing on magnetic resonance (MR) images of NPC patients. MRI changes were analyzed before and after the patient received radiotherapy. The image segmentation and radiotherapy effects of CNN were evaluated by the Recall, intersection over union (IOU), postoperative apparent diffusion coefficient (ADC), and diagnostic coincidence rate. Moreover, gradient vector flow (GVF) algorithm, fuzzy c-means (FCM), and SegNet were adopted for comparative evaluation. Recall of CNN was 94.89% and the IOU was 84.16%, which was remarkably different from other algorithms (P < 0.05). After analysis of the MRI images of patients receiving radiotherapy, ADC of local residual patients was 1.108 ± 0.097 measured by CNN, the ADC was 1.826 ± 0.115, and the missed diagnosis rate was only 7.14%. In summary, CNN had a good effect on the localization and segmentation of NPC patients, and can accurately evaluate the effect of patients receiving radiotherapy, which can assist clinical diagnosis and treatment of NPC.

BackgroundThe study of plant photosynthesis is essential for productivity and yield. Thanks to the development of high-throughput phenotyping (HTP) facilities, based on chlorophyll fluorescence imaging, photosynthetic traits can be measured in a reliable, reproducible and efficient manner. In most state-of-the-art HTP platforms, these traits are automatedly analyzed at individual plant level, but information at leaf level is often restricted by the use of manual annotation. Automated leaf tracking over time is therefore highly desired. Methods for tracking individual leaves are still uncommon, convoluted, or require large datasets. Hence, applications and libraries with different techniques are required. New phenotyping platforms are initiated now more frequently than ever; however, the application of advanced computer vision techniques, such as convolutional neural networks, is still growing at a slow pace. Here, we provide a method for leaf segmentation and tracking through the fine-tuning of Mask R-CNN and intersection over union as a solution for leaf tracking on top-down images of plants. We also provide datasets and code for training and testing on both detection and tracking of individual leaves, aiming to stimulate the community to expand the current methodologies on this topic.ResultsWe tested the results for detection and segmentation on 523 Arabidopsis thaliana leaves at three different stages of development from which we obtained a mean F-score of 0.956 on detection and 0.844 on segmentation overlap through the intersection over union (IoU). On the tracking side, we tested nine different plants with 191 leaves. A total of 161 leaves were tracked without issues, accounting to a total of 84.29% correct tracking, and a Higher Order Tracking Accuracy (HOTA) of 0.846. In our case study, leaf age and leaf order influenced photosynthetic capacity and photosynthetic response to light treatments. Leaf-dependent photosynthesis varies according to the genetic background.ConclusionThe method provided is robust for leaf tracking on top-down images. Although one of the strong components of the method is the low requirement in training data to achieve a good base result (based on fine-tuning), most of the tracking issues found could be solved by expanding the training dataset for the Mask R-CNN model.

Accurate lesion segmentation is critical for cancer diagnosis and treatment. Convolutional neural networks (CNNs) are widely used for medical image segmentation but struggle to capture long-range dependencies. Transformers mitigate this limitation but come with high computational costs. Mamba, a state-space model (SSM), efficiently models long-range dependencies but lacks precision in fine details. To address these challenges, this study aimed to develop a novel segmentation approach that combines the strengths of CNNs, Transformers, and Mamba, enhancing both global context understanding and local feature extraction in medical image segmentation. We propose AttmNet, a U-shaped network designed for medical image segmentation, which incorporates a novel structure called MAM (Multiscale-Convolution, Self-Attention, and Mamba). The MAM block integrates multi-layer convolution for multi-scale feature learning with an Att-Mamba component that combines self-attention and Mamba to effectively capture global context while preserving fine details. We evaluated AttmNet on four public datasets for breast, skin, and lung lesion segmentation. AttmNet outperformed state-of-the-art methods in terms of intersection over union (IoU) and Dice similarity coefficients. On the breast ultrasound (BUS) dataset, AttmNet achieved a 3.38% improvement in IoU and a 4.54% increase in Dice over the next best method. On the breast ultrasound images (BUSI) dataset, AttmNet's IoU and Dice coefficients were 1.17% and 3.21% higher than the closest competitor, respectively. In the PH2 Dermoscopy Image dataset, AttmNet surpassed the next best model by 0.25% in both IoU and Dice. On the larger coronavirus disease 2019 (COVID-19) Lung dataset, AttmNet maintained strong performance, achieving higher IoU and Dice scores than the next best models, SegMamba and TransUNet. AttmNet is a powerful and efficient tool for medical image segmentation, addressing the limitations of existing methods through its advanced design. The MAM block significantly enhances segmentation accuracy while maintaining computational efficiency, making AttmNet highly suitable for clinical applications. The code is available at https://github.com/hyb2840/AttmNet.

In the field of integrated circuits (IC) reverse engineering, accurate IC die image segmentation is critical for ensuring trust and detecting counterfeits. This study introduces a deep learning-based methodology utilizing a U-Net Convolutional Neural Network (CNN) tailored for IC image segmentation. Our model excels in processing complex IC images with noise, achieving superior segmentation accuracy. The model was evaluated on a dataset of 512 × 512 pixel IC die images, achieving a mean Intersection over Union (IoU) of 81.2%, which is a significant improvement over traditional image processing techniques that achieve an IoU around 65.3%. During training, the model’s Dice Loss showed a sharp decrease, as depicted in the provided graph, highlighting the model’s ability to effectively learn and refine segmentation boundaries. Simultaneously, the training accuracy, as illustrated in the accompanying accuracy graph, improved steadily, reaching approximately 60% but still rising. This convergence of Dice Loss and the upward trend in accuracy demonstrate the model’s robust performance across varying noise levels and its effectiveness in producing precise segmentation outputs. This work underscores the effectiveness of CNNs, particularly the U-Net architecture, in enhancing the accuracy and reliability of IC die image analysis, paving the way for improved IC manufacturing quality assurance.

Funding Acknowledgements Type of funding sources: Private grant(s) and/or Sponsorship. Main funding source(s): Japan Organization of Occupational Health and Safety onbehalf J3D Introduction Improvements in cardiovascular imaging have contributed to the accurate diagnosis and treatment of cardiovascular diseases. In recent years, we can find a new demand from surgical departments, i.e., 3D heart models of patients for preoperative simulations. However, a 3D surgical model needs a detailed segmentation of heart structures from clinical images because these details are essential for achieving accurate polygon data of an exact 3D model. Thus, in most cases, manual segmentation is required for complicated heart shapes such as the chamber wall, valves, and papillary muscles, which causes a prolonged duration time. Purpose We aim to achieve an automated heart segmentation using a convolutional neural network (CNN) trained using deep learning techniques for the rapid creation of 3D surgical heart models of individual patients. Methods We constructed our original CNN program based on the latest artificial intelligence techniques and trained it to extract shapes of the heart from cardiac computed tomography (CT) images. The training data was 361 slices selected from CT scans of 10 patients. We used data augmentation to increase the amount and diversity of the data into 24,052 slices. The training result with the best Intersection over Union (IoU), one of the evaluation metrics used in deep learning, was saved. Finally, we used the best-trained CNN to construct 3D polygon data of two surgical cases of hypertrophic obstructive cardiomyopathy (HOCM) for preoperative assessments. Results The IoU attained 0.85 after deep learning. The time required to complete the 3D polygon data for the first HOCM case was 5 minutes for segmentation by the trained CNN and 3 hours for data correction by a human operator. Similarly, the time required for the second case was 5 minutes for segmentation without manual correction. We had to correct the segmentation for the first case because we needed an exact 3D model for the preoperative assessment (Fig. 1). According to our records of the other eight 3D heart models in the lab, the work for a 3D polygon shape from CT images needs a median 30 hours (quartiles 23-50 hours) when the procedure is fully manual and non-continuous with breaks in between. Conclusion The CNN-based segmentation aided the constructing heart shapes from cardiac CT images of preoperative patients. Although the performance, reaching IoU of 0.85, was insufficient for fully automatic segmentation, the methodology can shorten the process duration from several hours to several minutes for detailed segmentation of heart structures. We previously applied the CNN-based segmentation to the aorta, aortic stenosis valves, and atheromatous plaque in clinical images, demonstrating adequate segmentation performance. The proposed methodology can be applied as a fundamental technology of cardiovascular imaging for obtaining the actual structures of a target object as 3D coordinate values or a 3D model within a reasonable duration time. Abstract Figure 1

The report presents an algorithm for constructing a convolutional hierarchical neural network classifier, which is a modification of the algorithm for constructing hierarchical neural network classifiers suggested before. The original algorithm was designed to exploit intrinsic class hierarchy to build a class tree with a neural network in each node classifying groups of initial classes (in a non-terminal node) or a subset of original classes (in a terminal node). The convolutional modification utilizes convolutional neural networks instead of regular fully connected networks in order to apply the model to image classification tasks. Use of class hierarchy for image classification should reduce the number of adjusted neural network parameters compared to deep convolutional neural networks, and therefore it should reduce training and inference time. In this context the algorithm may be compared with some pruning techniques. The convolutional hierarchical neural network classifier inherits some hyperparameters of a conventional hierarchical neural network classifier, like the activation threshold and the threshold by the share of voting patterns. The goal of this study was to explore different strategies of choosing these hyperparameters. To test these strategies, we used the CIFAR-10 dataset. Also, for demonstration purposes we apply the convolutional hierarchical neural network classifier to the CIFAR-100 dataset.

Recently, deep learning has greatly advanced medical image segmentation. Convolutional neural networks (CNNs) excel in capturing local image features, whereas ViT adeptly models long‐range dependencies through multi‐head self‐attention mechanisms. Despite their strengths, both CNN and ViT face challenges in efficiently processing long‐range dependencies in medical images and often require substantial computational resources. To address this, we propose a novel hybrid model combining Mamba and Transformer architectures. Our model integrates ViT's self‐attention modules within a pure‐vision Mamba U‐shaped encoder, capturing both global and local information through nested Transformer and Mamba modules. Additionally, a multi‐scale feed‐forward neural network is incorporated within the Mamba blocks to enhance feature diversity by capturing fine‐grained local details. Finally, a channel‐adaptive feature (CAF) fusion module is introduced at the original skip connections to mitigate feature loss during information fusion and to improve segmentation accuracy in boundary regions. Quantitative and qualitative experiments were conducted on two public datasets: breast ultrasound image (BUSI) and clinic. The Dice score, Intersection over Union (IoU) score, recall score, F 1 score and 95% Hausdorff distance (HD95) of the proposed model on the BUSI dataset were 0.7918, 0.7016, 0.8508, 0.7919 and 12.04 mm, respectively. On ClinicDB, these metrics reach 0.9239, 0.8671, 0.9278, 0.9239 and 5.49 mm, respectively. The proposed model outperforms existing state‐of‐the‐art CNN‐, Transformer‐ and Mamba‐based methods in segmentation accuracy, according to experimental data.

Brain tumors are formed due to the abnormal growth of the cells that get multiplied and become in uncontrollable perspective. Tumors can damage brain cells by pressing down the skull, which consociate begins in, which negatively affects human health. In advanced stages, a brain tumor is a more dangerous infection that cannot be relieved. Brain tumor detection and early prevention are necessary in today's world. An extreme learning machine (ELM) is a widely adopted algorithm in machine learning. It is proposed to use classification models in brain tumor imaging. This classification is based on the techniques implemented: Convolutional Neural Networks(CNN) and Generative Adversarial Networks(GAN). CNN efficiently solves the convex optimization problem and is faster, requiring less human effort. The algorithmic architecture of GAN uses two neural networks, pocking one against the other. These networks are implemented in various fields to classify brain Tumor images. The present study mainly aims to introduce a new proposed classification system of preschool children brain imaging with Hybrid Convolutional Neural Networks and with the techniques of GAN. The proposed technique is compared with the existing hybrid-CNN and hybrid-GAN techniques. The outcomes are encouraging because the loss is deduced, and the accuracy facet increases. The proposed system achieved a training accuracy of 97.8% and a validation accuracy of 89%. The outcomes of the studies show that ELM in the platform of GAN for preschool children brain imaging classification has achieved higher predictive performance than the traditional classification mechanisms in increasingly complex scenarios. Time elapsed for training brain images samples finds inference value for training samples and time elapsed value increased by 28.9855%. Approximation ratio for cost based on probability, finding Approximation ratio for low probability range is increased by 88.1%. The combination of CNN, GAN and hybrid-CNN, hybrid-GAN, and hybrid CNN + GAN, compared with the proposed hybrid system, increased Detection latency for low range learning rate by 3.31%.

Understanding the spatial distribution of irrigated croplands is crucial for food security and water use. To map land cover classes with high-spatial-resolution images, it is necessary to analyze the semantic information of target objects in addition to the spectral or spatial–spectral information of local pixels. Deep convolutional neural networks (DCNNs) can characterize the semantic features of objects adaptively. This study uses DCNNs to extract irrigated croplands from Sentinel-2 images in the states of Washington and California in the United States. We integrated the DCNNs of 101 layers, discarded pooling layers, and employed dilation convolution to preserve location information; these are models which were used based on fully convolutional network (FCN) architectures. The findings indicated that irrigated croplands may be effectively detected at various phases of crop growth in the fields. A quantitative analysis of the trained models revealed that the three models in the two states had the lowest values of Intersection over Union (IoU) and Kappa, i.e., 0.88 and 0.91, respectively. The deep models’ temporal portability across different years was acceptable. The lowest values of recall and OA (overall accuracy) from 2018 to 2021 were 0.91 and 0.87, respectively. In Washington, the lowest OA value from 10 to 300 m resolution was 0.76. This study demonstrates the potential of FCNs + DCNNs approaches for mapping irrigated croplands across large regions, providing a solution for irrigation mapping. The spatial resolution portability of deep models could be improved further by designing model architectures.

Recovering and distinguishing different ionospheric layers and signals usually requires slow and complicated procedures. In this work, we construct and train five convolutional neural network (CNN) models: DeepLab, fully convolutional DenseNet24 (FC-DenseNet24), deep watershed transform (DWT), Mask R-CNN, and spatial attention-UNet (SA-UNet) for the recovery of ionograms. The performance of the models is evaluated by intersection over union (IoU). We collect and manually label 6131 ionograms, which are acquired from a low-latitude ionosonde in Taiwan. These ionograms are contaminated by strong quasi-static noise, with an average signal-to-noise ratio (SNR) equal to 1.4. Applying the five models to these noisy ionograms, we show that the models can recover useful signals with IoU > 0.6. The highest accuracy is achieved by SA-UNet. For signals with less than 15% of samples in the data set, they can be recovered by Mask R-CNN to some degree (IoU > 0.2). In addition to the number of samples, we identify and examine the effects of three factors: (1) SNR, (2) shape of signal, (3) overlapping of signals on the recovery accuracy of different models. Our results indicate that FC-DenseNet24, DWT, Mask R-CNN and SA-UNet are capable of identifying signals from very noisy ionograms (SNR < 1.4), overlapping signals can be well identified by DWT, Mask R-CNN and SA-UNet, and that more elongated signals are better identified by all models.

Concrete cracks significantly impact structural durability by allowing harmful elements such as carbon dioxide and chloride ions to penetrate. Thus, the cracks should be prevented and treated to maintain the lifespan of concrete structures. Convolutional neural network (CNN) has been widely applied in crack detection. This study investigated the use of a Bayesian convolutional neural network (BCNN) for concrete crack classification and compared the performance of BCNN with UNet, a deep CNN-based model, and CNN. BCNN weight uncertainty through a Bayesian method, improved prediction reliability and prevented overfitting. BCNN demonstrated superior expected calibration error (0.0030) and Brier score (0.0025) compared to CNN, highlighting its robustness in prediction reliability. Furthermore, Bayesian entropy analysis confirmed the ability of BCNN to quantify uncertainty, leading to more precise crack classification. These results imply that BCNN provides a balance between computational efficiency and performance, offering strong potential for smart construction applications. Validation results indicated that BCNN achieved a validation accuracy of 92.22%, higher than CNN (85.78%) and slightly lower than UNet (94.34%). However, BCNN demonstrated a superior intersection over union (IoU) of 0.8049, outperforming CNN (0.6685) and approaching UNet (0.9135). These results implied that BCNN provides a balance between computational efficiency and performance, offering strong potential for smart construction applications.

Background: Optical coherence tomography is a powerful modality to assess atherosclerotic lesions, but detecting lesions in high-resolution OCT is challenging and requires expert knowledge. Deep-learning algorithms can be used to automatically identify atherosclerotic lesions, facilitating identification of patients at risk. We trained a deep-learning algorithm (DeepAD) with co-registered, annotated histopathology to predict atherosclerotic lesions in optical coherence tomography (OCT).Methods: Two datasets were used for training DeepAD: (i) a histopathology data set from 7 autopsy cases with 62 OCT frames and co-registered histopathology for high quality manual annotation and (ii) a clinical data set from 51 patients with 222 OCT frames in which manual annotations were based on clinical expertise only. A U-net based deep convolutional neural network (CNN) ensemble was employed as an atherosclerotic lesion prediction algorithm. Results were analyzed using intersection over union (IOU) for segmentation.Results: DeepAD showed good performance regarding the prediction of atherosclerotic lesions, with a median IOU of 0.68 ± 0.18 for segmentation of atherosclerotic lesions. Detection of calcified lesions yielded an IOU = 0.34. When training the algorithm without histopathology-based annotations, a performance drop of >0.25 IOU was observed. The practical application of DeepAD was evaluated retrospectively in a clinical cohort (n = 11 cases), showing high sensitivity as well as specificity and similar performance when compared to manual expert analysis.Conclusion: Automated detection of atherosclerotic lesions in OCT is improved using a histopathology-based deep-learning algorithm, allowing accurate detection in the clinical setting. An automated decision-support tool based on DeepAD could help in risk prediction and guide interventional treatment decisions.

Latest advances in artificial intelligence, particularly in object recognition and segmentation, provide unprecedented opportunities for precision agriculture. This work investigates the use of state-of-the-art AI models, namely Meta’s Segment Anything (SAM) and GroundingDino, for the task of grape cluster detection in vineyards. Three different methods aimed at enhancing the instance segmentation process are proposed: (i) SAM-Refine (SAM-R), which refines a previously proposed depth-based clustering approach, referred to as DepthSeg, using SAM; (ii) SAM-Segmentation (SAM-S), which integrates SAM with a pre-trained semantic segmentation model to improve cluster separation; and (iii) AutoSAM-Dino (ASD), which eliminates the need for manual labeling and transfer learning through the combined use of GroundingDino and SAM. Analysis is conducted on both the object counting and pixel-level segmentation accuracy against a manually labeled ground truth. Metrics such as mean Average Precision (mAP), Intersection over Union (IoU), and precision and recall are calculated to assess the system performance. Compared to the original DepthSeg algorithm, SAM-R slightly advances object counting (mAP: +0.5%) and excels in pixel-level segmentation (IoU: +17.0%). SAM-S, despite a mAP decrease, improves segmentation accuracy (IoU: +13.9%, Precision: +9.2%, Recall: +11.7%). Similarly, ASD, although with a lower mAP, shows significant accuracy enhancement (IoU: +7.8%, Precision: +4.2%, Recall: +4.9%). Additionally, from a labor effort point of view, instance segmentation techniques require much less time for training than manual labeling.