Precise Lung Cancer Prediction using ResNet – 50 Deep Neural Network Architecture

Assess the efficacy of deep convolutional neural networks (DCNNs) in detection of critical enteric feeding tube malpositions on radiographs. 5475 de-identified HIPAA compliant frontal view chest and abdominal radiographs were obtained, consisting of 174 x-rays of bronchial insertions and 5301 non-critical radiographs, including normal course, normal chest, and normal abdominal x-rays. The ground-truth classification for enteric feeding tube placement was performed by two board-certified radiologists. Untrained and pretrained deep convolutional neural network models for Inception V3, ResNet50, and DenseNet 121 were each employed. The radiographs were fed into each deep convolutional neural network, which included untrained and pretrained models. The Tensorflow framework was used for Inception V3, ResNet50, and DenseNet. Images were split into training (4745), validation (630), and test (100). Both real-time and preprocessing image augmentation strategies were performed. Receiver operating characteristic (ROC) and area under the curve (AUC) on the test data were used to assess the models. Statistical differences among the AUCs were obtained. p < 0.05 was considered statistically significant. The pretrained Inception V3, which had an AUC of 0.87 (95 CI; 0.80–0.94), performed statistically significantly better (p < .001) than the untrained Inception V3, with an AUC of 0.60 (95 CI; 0.52–0.68). The pretrained Inception V3 also had the highest AUC overall, as compared with ResNet50 and DenseNet121, with AUC values ranging from 0.82 to 0.85. Each pretrained network outperformed its untrained counterpart. (p < 0.05). Deep learning demonstrates promise in differentiating critical vs. non-critical placement with an AUC of 0.87. Pretrained networks outperformed untrained ones in all cases. DCNNs may allow for more rapid identification and communication of critical feeding tube malpositions.

Recognition of human activity based on convolutional neural network has received the interest of researchers in recent years due to its significant improvement in accuracy. A large number of algorithms based on the deep learning approach have been proposed for activity recognition purpose. However, with the increasing advancements in technologies having limited computational resources, it needs to design an efficient deep learning-based approaches with improved utilization of computational resources. This paper presents a simple and efficient 2-Dimensional convolutional neural network (2-D CNN) architecture with very small size convolutional kernel for human activity recognition. The merit of the proposed CNN architecture over standard deep learning architectures is fewer trainable parameters and lesser memory requirement which enables it to train the proposed CNN architecture on low GPU memory-based devices and also works well with smaller as well as larger size datasets. The proposed approach consists of mainly four stages: namely (1) creation of dataset and data augmentation, (2) designing 2-D convolutional neural network (CNN) architecture, (3) the proposed 2-D CNN architecture trained from scratch up to optimum stage, and (4) evaluation of the trained 2-D CNN architecture. To illustrate the effectiveness of the proposed architecture several extensive experiments are conducted on three publicly available datasets, namely IXMAS, YouTube, and UCF101 dataset. The results of the proposed method and its comparison with other state-of-the-art methods [8-12,14,18-26,29-33] demonstrate the usefulness of the proposed method.

Violence recognition is crucial because of its applications in activities related to security and law enforcement. Existing semi-automated systems have issues such as tedious manual surveillances, which causes human errors and makes these systems less effective. Several approaches have been proposed using trajectory-based, non-object-centric, and deep-learning-based methods. Previous studies have shown that deep learning techniques attain higher accuracy and lower error rates than those of other methods. However, the their performance must be improved. This study explores the state-of-the-art deep learning architecture of convolutional neural networks (CNNs) and inception V4 to detect and recognize violence using video data. In the proposed framework, the keyframe extraction technique eliminates duplicate consecutive frames. This keyframing phase reduces the training data size and hence decreases the computational cost by avoiding duplicate frames. For feature selection and classification tasks, the applied sequential CNN uses one kernel size, whereas the inception v4 CNN uses multiple kernels for different layers of the architecture. For empirical analysis, four widely used standard datasets are used with diverse activities. The results confirm that the proposed approach attains 98% accuracy, reduces the computational cost, and outperforms the existing techniques of violence detection and recognition.

From independently conducted free-response receiver operating characteristic (FROC) and receiver operating characteristic (ROC) experiments, to study fixed-reader associations between three estimators: the area under the alternative FROC (AFROC) curve computed from FROC data, the area under the ROC curve computed from FROC highest rating data, and the area under the ROC curve computed from confidence-of-disease ratings. Two hundred mammograms, 100 of which were abnormal, were processed by two image-processing algorithms and interpreted by four radiologists under the FROC paradigm. From the FROC data, inferred-ROC data were derived, using the highest rating assumption. Eighteen months afterwards, the images were interpreted by the same radiologists under the conventional ROC paradigm; conventional-ROC data (in contrast to inferred-ROC data) were obtained. FROC and ROC (inferred, conventional) data were analyzed using the nonparametric area-under-the-curve (AUC), (AFROC and ROC curve, respectively). Pearson correlation was used to quantify the degree of association between the modality-specific AUC indices and standard errors were computed using the bootstrap-after-bootstrap method. The magnitude of the correlations was assessed by comparison with computed Obuchowski-Rockette fixed reader correlations. Average Pearson correlations (with 95% confidence intervals in square brackets) were: Corr(FROC, inferred ROC) = 0.76[0.64, 0.84] > Corr(inferred ROC, conventional ROC) = 0.40[0.18, 0.58] > Corr (FROC, conventional ROC) = 0.32[0.16, 0.46]. Correlation between FROC and inferred-ROC data AUC estimates was high. Correlation between inferred- and conventional-ROC AUC was similar to the correlation between two modalities for a single reader using one estimation method, suggesting that the highest rating assumption might be questionable.

In this paper, we study the performance of variants of well-known Convolutional Neural Network (CNN) architectures on different audio tasks. We show that tuning the Receptive Field (RF) of CNNs is crucial to their generalization. An insufficient RF limits the CNN's ability to fit the training data. In contrast, CNNs with an excessive RF tend to over-fit the training data and fail to generalize to unseen testing data. As state-of-the-art CNN architectures - in computer vision and other domains - tend to go deeper in terms of number of layers, their RF size increases and therefore they degrade in performance in several audio classification and tagging tasks. We study well-known CNN architectures and how their building blocks affect their receptive field. We propose several systematic approaches to control the RF of CNNs and systematically test the resulting architectures on different audio classification and tagging tasks and datasets. The experiments show that regularizing the RF of CNNs using our proposed approaches can drastically improve the generalization of models, out-performing complex architectures and pre-trained models on larger datasets. The proposed CNNs achieve state-of-the-art results in multiple tasks, from acoustic scene classification to emotion and theme detection in music to instrument recognition, as demonstrated by top ranks in several pertinent challenges (DCASE, MediaEval).

Every year, new Convolutional Neural Network (CNN) architectures appear to deal with different problems in the activity of image and video recognition. These architectures usually work along the ImageNet dataset for looking for the best performance of the CNNs without taking into account the video task where they are used. This can represent a problem if the task is Human Action Recognition (HAR) in video, since the CNN architectures are pre-trained with an image dataset that can practically contain any object, while HAR problem requires consecutive frames of people doing actions. To prove the idea that using CNNs pre-trained on an image dataset does not always achieve the best performance on a video dataset and that, therefore, it is worth comparing the performance of different CNNs under similar circumstances for the HAR problem, this work proposes an analysis between eight different CNN architectures. Each one of the CNN was exclusively trained with RGB images, which were extracted from the frames of the different classes of videos of HMDB51 dataset. To make the classification of an activity in video, we average the predictions taking into account the successes. We also made some ensembles with the best performance CNNs to measure the improvement in accuracy. Our results suggest that Xception is a strong baseline model that could be used by the community to make their comparisons of their proposals more robust.

In this research, we present a new way of thinking using a Convolutional Neural Network (CNN) for palm-vein biometric authentication. In contrary to fingerprint and face, palm vein patterns are internal features which make them very hard to replicate. The objective of this research is to examine the possibility of a contactless authentication of individuals by imply a series of palm veins photographs taken by a camera in the near infrared. Biometric systems based on palm veins are considered very promising for high security environments. In mean time, deep learning techniques have assisted in image classification and tasks retrieval. The use of palm vein recognition through deep learning based methods and Convolutional Neural Network architectures (i.e., Inception V3 and SmallerVggNet) applications.

In this paper, the Convolutional Neural Network (CNN) architecture, which is one of the deep learning architectures, is used to classify the basic circuit components drawn by hand. During the training and testing stages of the model, a new dataset containing images of 863 circuit components manually drawn by different people is created. The data set contains images of four different classes of circuit components such as resistor, inductor, capacitor and voltage source. All images have been fixed to the same size and converted to grayscale to increase recognition performance and reduce process complexity. In the study, training for four classes is performed with CNN architecture. Based on the CNN architecture, four new CNN models are employed with different the number of layers. The training and validation results of these models are compared separately, the model with the highest training and validation performance is observed with four layer CNN model (CNN-4). This model obtained 84.41% accuracy rate at classification task.

In recent years, many researchers have focused on using convolutional neural networks to perform human activity recognition as evidenced by the emergence of a number of convolutional neural network architectures such as LeNet-5, AlexNet and VGG16 and modern architectures such as ResNet, Inception V3, Inception-ResNet, MobileNet V2, NASNet and PNASNet. The main characteristic of a convolutional neural network (CNN) is its ability to extract features automatically from input images, which facilitates the processes of activity recognition and classification. Convolutional networks indeed derive more relevant and complex features with every additional layer. In addition, CNNs have achieved perfect classification on highly similar activities that were previously extremely difficult to classify. In this paper, we evaluate modern convolutional neural networks in terms of their human activity recognition accuracy, and we compare the results with the state of the art methods. In our research, we used two public data sets, HMDB (Shooting gun, kicking, falling to the floor, punching) and the Weizman dataset (walking, running, jumping, bending, one hand waving, two-hand waving, jumping in place, jumping jack, skipping). Our experimental results indicated that the CNN with NASNet architecture achieves the best performance of the six CNN architectures on both human activity data sets (HMDB and Weizman).

Simple SummaryThe assistance of computer image analysis that automatically identifies tissue or cell types has greatly improved histopathologic interpretation and diagnosis accuracy. In this paper, the Convolutional Neural Network (CNN) has been adapted to predict and classify lymph node metastasis in breast cancer. We observe that image resolutions of lymph node metastasis datasets in breast cancer usually are quite smaller than the designed model input resolution, which defects the performance of the proposed model. To mitigate this problem, we propose a boosted CNN architecture and a novel data augmentation method called Random Center Cropping (RCC). Different from traditional image cropping methods only suitable for resolution images in large scale, RCC not only enlarges the scale of datasets but also preserves the resolution and the center area of images. In addition, the downsampling scale of the network is diminished to be more suitable for small resolution images. Furthermore, we introduce attention and feature fusion mechanisms to enhance the semantic information of image features extracted by CNN. Experiments illustrate that our methods significantly boost performance of fundamental CNN architectures, where the best-performed method achieves an accuracy of 97.96% ± 0.03% and an Area Under the Curve (AUC) of 99.68% ± 0.01% in Rectified Patch Camelyon (RPCam) datasets, respectively.(1) Purpose: To improve the capability of EfficientNet, including developing a cropping method called Random Center Cropping (RCC) to retain the original image resolution and significant features on the images’ center area, reducing the downsampling scale of EfficientNet to facilitate the small resolution images of RPCam datasets, and integrating attention and Feature Fusion (FF) mechanisms with EfficientNet to obtain features containing rich semantic information. (2) Methods: We adopt the Convolutional Neural Network (CNN) to detect and classify lymph node metastasis in breast cancer. (3) Results: Experiments illustrate that our methods significantly boost performance of basic CNN architectures, where the best-performed method achieves an accuracy of 97.96% ± 0.03% and an Area Under the Curve (AUC) of 99.68% ± 0.01% on RPCam datasets, respectively. (4) Conclusions: (1) To our limited knowledge, we are the only study to explore the power of EfficientNet on Metastatic Breast Cancer (MBC) classification, and elaborate experiments are conducted to compare the performance of EfficientNet with other state-of-the-art CNN models. It might provide inspiration for researchers who are interested in image-based diagnosis using Deep Learning (DL). (2) We design a novel data augmentation method named RCC to promote the data enrichment of small resolution datasets. (3) All of our four technological improvements boost the performance of the original EfficientNet.

Detection of metastasis of mediastinal lymph nodes in lung cancer patients with an artificial intelligence model.

This research proposes a state-of-the-art method of automated lung cancer risk prediction using the union of convolutional neural networks (CNNs) and radiomic feature extraction from low dose computed tomography (LDCT) scans. From the publicly accessible LIDC-IDRI dataset containing over 1,000 labeled LDCT scans having rich data about lung nodules, this study isolates quantitative radiomic features such as texture, shape, intensity, and edge sharpness employing software like 3D Slicer and Pyradiomics. Four deep CNN models—VGG16, VGG19, ResNet50, and Inception V3 are trained and validated with TensorFlow and PyTorch platforms. The models are differentiated on accuracy, precision, recall, and F1-score. Inception V3 is the highest in accuracy of 98.67%, then ResNet50 (94.50%), VGG19 (90.23%), and VGG16 (88.56%). The confusion matrices also validate the robustness of each model to differentiate between lung cancer stages. Radiomic feature integration through handcrafted and deep learning have significantly improved classification accuracy, enabling a strong framework for early lung cancer detection and risk stratification. The hybrid method has clinical translation potential through enhanced diagnosis accuracy and patient-specific treatment planning. The study concludes that AI-based methods of this type have the potential to be effective tools in modern medical imaging and cancer diagnosis pipelines.

The convolutional neural network (CNN) architecture has shown remarkable success in image classification and segmentation. Its popularity has increased promptly due to various factors such as exponential growth in computational resources, availability of benchmark datasets, supporting libraries, and open-source software. The efficiency of CNN architecture majorly depends on the complexity of the architecture, availability of datasets, and hyperparameter selection. But, due to the huge number of parameters in CNN architecture, its selection has completely remained ad hoc in the past works. In this article, a novel encoding technique has been proposed that can represent complex CNN architecture effectively. The article defines basic building blocks to represent CNN architecture such as the genesis block, transit block, agile block, and output block. This encoding structure is used to generate dynamic length chromosome structure and initialized using evolutionary algorithms. A comparative analysis is also presented that shows compared its effectiveness with existing encoding representations on the basis of the number of encoding parameters, training cost, and efficiency.

Bangla handwritten character recognition is a popular research topic as its difficulty is higher than the recognition of other languages because of multiple formats of compound characters. State of the art Convolutional neural network (CNN) architectures are very much useful in computer vision applications. Some works have been carried out in Bangla handwritten character recognition but most of them either not very efficient or they can not classify a lot of characters. In this work, state of art pre-trained CNN architectures is used to classify 231 different Bangla handwritten characters using CMATERdb dataset. The images were first converted to B&W form with white as the foreground color. The size of the images is reduced to 28 × 28 form. These images are used as input to the CNN architectures. The weights of the state-of-the-art CNN models are kept as it was. The training learning rate was set to 0.001 and categorical cross-entropy as the error function. After 50 epochs, InceptionResNetV2 achieved the best accuracy (96.99%). DenseNet121 and InceptionNetV3 also provided remarkable recognition accuracy (96.55 and 96.20%, respectively). We also considered combination of trained InceptionResNetV2, InceptionNetV3 and DenseNet121 architectures which provided better recognition accuracy (97.69%) than other single CNN architectures but it is not feasible for using as it requires a lot of computation power and memory. The models were tested in the cases where characters look confusing to humans, but all the architectures showed equal capability in recognizing these images. Considering computational complexity, memory and capability of recognizing confused characters, InceptionResNetV2 can be said as the best performing model.

Classification performance based on ImageNet is the de-facto standard metric for CNN development. In this work we challenge the notion that CNN architecture design solely based on ImageNet leads to generally effective convolutional neural network (CNN) architectures that perform well on a diverse set of datasets and application domains. To this end, we investigate and ultimately improve ImageNet as a basis for deriving such architectures. We conduct an extensive empirical study for which we train 500 CNN architectures, sampled from the broad AnyNetX design space, on ImageNet as well as 8 additional well-known image classification benchmark datasets from a diverse array of application domains. We observe that the performances of the architectures are highlydataset dependent. Some datasets even exhibit a negative error correlation with ImageNet across all architectures. We show how to significantly increase these correlations byutilizing ImageNet subsets restricted to fewer classes. These contributions can have a profound impact on the way we design future CNN architectures and help alleviate the tilt we see currently in our community with respect to over-reliance on one dataset.