A novel ResNet101 model based on dense dilated convolution for image classification

Image classification plays an important role in computer vision. The existing convolutional neural network methods have some problems during image classification process, such as low accuracy of tumor classification and poor ability of feature expression and feature extraction. Therefore, we propose a novel ResNet101 model based on dense dilated convolution for medical liver tumors classification. The multi-scale feature extraction module is used to extract multi-scale features of images, and the receptive field of the network is increased. The depth feature extraction module is used to reduce background noise information and focus on effective features of the focal region. To obtain broader and deeper semantic information, a dense dilated convolution module is deployed in the network. This module combines the advantages of Inception, residual structure, and multi-scale dilated convolution to obtain a deeper level of feature information without causing gradient explosion and gradient disappearance. To solve the common feature loss problems in the classification network, the up- down-sampling module in the network is improved, and multiple convolution kernels with different scales are cascaded to widen the network, which can effectively avoid feature loss. Finally, experiments are carried out on the proposed method. Compared with the existing mainstream classification networks, the proposed method can improve the classification performance, and finally achieve accurate classification of liver tumors. The effectiveness of the proposed method is further verified by ablation experiments.Highlights The multi-scale feature extraction module is introduced to extract multi-scale features of images, it can extract deep context information of the lesion region and surrounding tissues to enhance the feature extraction ability of the network. The depth feature extraction module is used to focus on the local features of the lesion region from both channel and space, weaken the influence of irrelevant information, and strengthen the recognition ability of the lesion region. The feature extraction module is enhanced by the parallel structure of dense dilated convolution, and the deeper feature information is obtained without losing the image feature information to improve the classification accuracy.

Follow-up Photometry in Another Band Helps to Reduce Kepler’s False-positive Rates

The Kepler mission’s single-band photometry suffers from astrophysical false positives, most commonly of background eclipsing binaries (BEBs) and companion transiting planets (CTPs). Multicolor photometry can reveal the color-dependent depth feature of false positives and thus exclude them. In this work, we aim to estimate the fraction of false positives that cannot be classified by Kepler alone but can be identified from their color-dependent depth feature if a reference band (z, K s , and Transiting Exoplanet Survey Satellite (TESS)) is adopted in follow-up observation. We construct physics-based blend models to simulate multiband signals of false positives. Nearly 65%–95% of the BEBs and more than 80% of the CTPs that host a Jupiter-sized planet will show detectable depth variations if the reference band can achieve a Kepler-like precision. The K s band is most effective in eliminating BEBs exhibiting features of any depth, while the z and TESS bands are better for identifying giant candidates, and their identification rates are more sensitive to photometric precision. Given the radius distribution of planets transiting the secondary star in binary systems, we derive a formalism to calculate the overall identification rate for CTPs. By comparing the likelihood distribution of the double-band depth ratio for BEB and planet models, we calculate the false-positive probability (FPP) for typical Kepler candidates. Additionally, we show that the FPP calculation helps distinguish the planet candidate’s host star in an unresolved binary system. The framework of the analysis in this paper can be easily adapted to predict the multicolor photometric yield for other transit surveys, especially TESS.

Multi-Modal Remote Sensing Image Matching Method Based on Deep Learning Technology

Remote sensing is a scientific technology that uses sensors to detect the reflection, radiation or scattering of electromagnetic wave signals from ground objects in a non-contact and long-distance manner. The images are classified by the extracted image feature information Recognition is a further study of obtaining target feature information, which is of great significance to urban planning, disaster monitoring, and ecological environment evaluation. The image matching framework proposed in this paper matches the depth feature maps, and reversely pushes the geometric deformation between the depth feature maps to between the original reference image and the target image, and eliminates the geometric deformation between the original images. Finally, through feature extraction of the corrected image, the extracted local feature image blocks are input into the trained multi-modal feature matching network to complete the entire matching process. Experiments show that the negative sample set construction strategy that takes into account the sample distance proposed in this experiment can effectively deal with the problem of neighboring point interference in RSI matching, and improve the matching performance of the network model.

An Automated In-Depth Feature Learning Algorithm for Breast Abnormality Prognosis and Robust Characterization from Mammography Images Using Deep Transfer Learning

Background: Diagnosing breast cancer masses and calcification clusters have paramount significance in mammography, which aids in mitigating the disease’s complexities and curing it at early stages. However, a wrong mammogram interpretation may lead to an unnecessary biopsy of the false-positive findings, which reduces the patient’s survival chances. Consequently, approaches that learn to discern breast masses can reduce the number of misconceptions and incorrect diagnoses. Conventionally used classification models focus on feature extraction techniques specific to a particular problem based on domain information. Deep learning strategies are becoming promising alternatives to solve the many challenges of feature-based approaches. Methods: This study introduces a convolutional neural network (ConvNet)-based deep learning method to extract features at varying densities and discern mammography’s normal and suspected regions. Two different experiments were carried out to make an accurate diagnosis and classification. The first experiment consisted of five end-to-end pre-trained and fine-tuned deep convolution neural networks (DCNN). The in-depth features extracted from the ConvNet are also used to train the support vector machine algorithm to achieve excellent performance in the second experiment. Additionally, DCNN is the most frequently used image interpretation and classification method, including VGGNet, GoogLeNet, MobileNet, ResNet, and DenseNet. Moreover, this study pertains to data cleaning, preprocessing, and data augmentation, and improving mass recognition accuracy. The efficacy of all models is evaluated by training and testing three mammography datasets and has exhibited remarkable results. Results: Our deep learning ConvNet+SVM model obtained a discriminative training accuracy of 97.7% and validating accuracy of 97.8%, contrary to this, VGGNet16 method yielded 90.2%, 93.5% for VGGNet19, 63.4% for GoogLeNet, 82.9% for MobileNetV2, 75.1% for ResNet50, and 72.9% for DenseNet121. Conclusions: The proposed model’s improvement and validation are appropriated in conventional pathological practices that conceivably reduce the pathologist’s strain in predicting clinical outcomes by analyzing patients’ mammography images.

Research on Basketball Goal Recognition Based on Image Processing and Improved Algorithm

This paper studies the basketball goal recognition method based on image processing and improved algorithm to improve the accuracy of automatic recognition of basketball goal. The infrared spectrum image acquisition system is used to collect the basketball goal image. After the image is denoised by using the adaptive filtering algorithm, the wavelet analysis method is used to extract the features of basketball goal signal, which are input into the optimized deformable convolution neural network. Through the weighted sum of the values of each sampling point and the corresponding position authority of the block convolution core, the results are output as convolution operation. Combined with the depth feature of the same dimension, the full connection feature of the candidate target area is obtained to realize the basketball goal recognition. The experimental results show the following: the method can effectively identify basketball goals and the recognition error rate is low; the average accuracy of the automatic recognition results of basketball goals is as high as 98.4%; under the influence of different degrees of noise, the method is less affected by noise and has strong anti-interference ability.

Depth feature extraction of surface-breaking defects using laser pulsed thermography

In this paper, the feasibility of the depth feature extraction of surface-breaking defects based on laser pulsed thermography in transmission mode is proposed. This method is adopted to detect the depth of surface-breaking defects. First, a finite element model based on COMSOL is established to simulate structural steel specimens with different defect depths and the contrast method is used to detect these depths. In order to verify its feasibility, the model is simulated at different time nodes. A simulation analysis shows the practicability of this method. Then, through an artificial slot depth feature extraction experiment on structural steel specimens, an algorithm is used to denoise an infrared image and then the contrast method is used to extract the defect depth feature, which verifies the feasibility of the method. The experimental results show that as the depth of artificial slots increases, the temperature at the observation point also reduces. The best fitting equation of the defect depth and the temperature at the observation point at a certain heating time have an exponential relationship. This method can accurately detect defect depths.