A Convolutional Neural Network Based Classification Approach for Breast Cancer Detection

Graph convolutional network – Long short term memory neural network- multi layer perceptron- Gaussian progress regression model: A new deep learning model for predicting ozone concertation

Handwritten digit recognition, a task in computer vision, is critical for applications such as postal automation, banking, and digitization of forms. Traditional approaches have leveraged statistical models, but the rise of deep learning, particularly Convolutional Neural Networks (CNN) and Long-Short Term Memory (LSTM), has revolutionized the field. However, a comprehensive performance comparison of CNN and LSTM architectures in the context of handwritten digit classification remains underexplored. This study aimed to address this gap by evaluating and comparing CNN and LSTM models for the classification of handwritten digits. Two machine learning models – Convolutional Neural Network (CNN) and Long-Short Term Memory (LSTM) were trained with the preprocessed handwritten digit dataset. The CNN model was designed with multiple convolutional and pooling layers, along with dropout for regularization. The LSTM model was designed with LSTM layers to capture sequential patterns in the data, followed by a dense layer for classification. The models were implemented in python, evaluated and compared based on accuracy, precision, recall and F1-score. The evaluation and comparison results indicate that CNN achieved 99.31% accuracy, 99.0% precision, 99.0% recall, and a 99.0% F1-score, while LSTM achieved 98.90% accuracy, 99.0% precision, 99.0% recall, and a 99.0% F1-score. The results demonstrated that CNN outperformed LSTM in terms of accuracy and misclassification errors, making it the optimal choice for image-based handwritten digit recognition. This finding underscores the efficiency of CNN in addressing challenges related to digit recognition, contributing to the advancement of automated digit classification systems and improving the accuracy of image-based classification tasks.

Classification can significantly impact treatment decisions and patient outcomes. This study evaluates and compares the performance of three machine learning models Extreme Learning Machine (ELM), Long Short-Term Memory (LSTM), and Convolutional Neural Networks (CNN) in breast cancer classification. ELM, known for its fast-learning speed and strong generalization, is compared with LSTM, which is effective in capturing long-term dependencies in sequential data, and CNN, which is renowned for its ability to automatically extract features from images and structured data. The models were trained and tested on a breast cancer dataset, focusing on accuracy and computational efficiency. The results revealed that while CNNs demonstrated better accuracy in feature-rich data, LSTMs excelled in handling sequential data patterns. On the other hand, ELM offers a good balance between training speed and classification performance. This comparative analysis provides valuable insights into the strengths and limitations of each model, contributing to the development of more effective breast cancer diagnostic tools. In this case, LSTM outperformed ELM by 0.91%, outperformed CNN significantly by 3.72%, and outperformed Improved LSTM by 0.91%. This indicate that the LSTM model shows higher accuracy in breast cancer classification

Land-cover and land-use classification from aerial images is a challenging problem due to high intra-class diversity and inter-class similarities of the images. To analyze the performances of deep convolutional neural network (CNN) models in this domain, we provide three pre-trained CNN models that are adapted to NWPU-RESISC45 dataset using three different training splits, i.e., 80%, 20%, and 10% ratios. The architectures of all three models are redesigned to be modest in size and their structure is kept simple, yet when tested with the NWPU-RESISC45 dataset, all three models perform comparably to the state-of-the-art models. Each of these models is then used to classify the scenes taken from five well-known datasets in this domain without any fine-tuning. We aim to assess the generalization capabilities of these models on the selected datasets. For better analysis, we considered using top-3 and top-5 accuracies of the models in addition to the best predicted category (top-1) that is usually reported by the models. This way of interpretation is very suitable in this domain, since the datasets contain a high number of fine grained categories with large semantic overlaps. We empirically show that the proposed CNN models actually learn the relevant semantic features in the aerial images better than we observed via standard measures. To the best of our knowledge, this is the first work in this domain that analyzes and presents model generalization performances the way we presented it here.

This research paper explores the hybrid models for Javanese character recognition using 15600 characters gathered from digital and handwritten sources. The hybrid model combines the merit of deep learning using convolutional neural networks (CNN) to involve feature extraction and a machine learning classifier using support vector machine (SVM). The dropout layer also manages overfitting problems and enhances training accuracy. For evaluation purposes, we also compared CNN models with three different architectures with multilayer perceptron (MLP) models with one and two hidden layer(s). In this research, we evaluated three variants of CNN architectures and the hybrid CNN-SVM models on both the accuracy of classification and training time. The experimental outcomes showed that the classification performances of all CNN models outperform the classification performances of both MLP models. The highest testing accuracy for basic CNN is 94.2% when using model 3 CNN. The increment of hidden layers to the MLP model just slightly enhances the accuracy. Furthermore, the hybrid model gained the highest accuracy result of 98.35% for classifying the testing data when combining model 3 CNN with the SVM classifier. We get that the hybrid CNN-SVM model can enhance the accuracy results in the Javanese characters recognition.

Understanding the learning mechanism of convolutional neural networks in spectral analysis

Date palm ( Phoenix dactylifera L.) is one of the most valuable domesticated fruit trees characterized with thousands of varieties that grow in different arid regions. Because of high diversification, discrimination between date varieties at different post-harvest handling and production stages is necessary. In this study, five different CNN (Convolutional Neural Network) models, namely ResNet18, ResNet50, MobileNet, GoogleNet and DenseNet, are used as fine-tuning tools for the classification of five Moroccan date fruit varieties: ‘Mejhoul’, ‘Boufeggous’, ‘Assiane’, ‘Aziza’ and ‘Bousthammi’. The features of MobileNet, the most successful of these CNN models, were analyzed with an RNN (Recurrent Neural Network)-based LSTM (Long short-term memory) architecture. In addition, feature selection is performed for MobileNet features to achieve a more successful classification with fewer features. As a result of LSTM-based classification of both original MobileNet features and selected features, higher classification accuracy was achieved in comparison with other CNN models. Moreover, LSTM with selected features provided the most successful discrimination ability. The accuracies obtained as a result of the classification of original MobileNet features and selected features with LSTM were 99.63% and 99.70% respectively. Overall, the results indicated that the LSTM-based architecture with fewer features improves the success of existing CNN models for date fruits.

Deep learning models for short-term travel speed prediction on urban expressways, such as the convolutional neural network (CNN), still present several limitations in multiscale spatiotemporal feature extraction. Hence, in this paper, three hybrid CNN models are proposed to improve the basic CNN model with regard to three target aspects for short-term (i.e., 5 min) travel speed prediction on urban expressways. More specifically, long short-term memory (LSTM), AutoEncoder (AE), and Inception module are incorporated into the basic CNN model to capture multiscale spatiotemporal features of travel speed data effectively and improve the accuracy and robustness of the basic CNN model. Based on loop detector data collected on the Yan’an expressway in Shanghai, the proposed hybrid CNN models are trained and tuned. To validate the improvements on the target aspects, a comprehensive comparison is conducted using a classical statistical model (i.e., autoregressive integrated moving average), a typical shallow neural network model (i.e., artificial neural network), and two basic deep learning models (i.e., recurrent neural network and CNN). Results show that the prediction accuracies of all the proposed hybrid CNN models exceed 96% and the mean absolute errors are less than 2.5 km/h, which are superior to other models. In terms of target improving aspects, two new metrics were introduced, and the proposed models, especially the AE–CNN model, showed better robustness under various input data structures and traffic states. The LSTM–CNN model outperformed the other models in learning time-series features, and the Inception–CNN model is superior in reproducing the dynamics of traffic congestion patterns on urban expressways.

This research aims to implement deep learning techniques to determine fact and fake news in Indonesian language. The methods used are Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM). The stages of the research consisted of collecting data, labeling data, preprocessing data, word embedding, splitting data, forming CNN and LSTM models, evaluating, testing new input data and comparing evaluations of the established CNN and LSTM models. The Data are collected from a fact and fake news provider site that is valid, namely TurnbackHoax.id. There are 1786 news used in this study, with 802 fact and 984 fake news. The results indicate that the CNN and LSTM methods were successfully applied to determine fact and fake news in Indonesian language properly. CNN has an accuracy test, precision and recall value of 0.88, while the LSTM model has an accuracy test and precision value of 0.84 and a recall of 0.83. In testing the new data input, all of the predictions obtained by CNN are correct, while the prediction results obtained by LSTM have 1 wrong prediction. Based on the evaluation results and the results of testing the new data input, the model produced by the CNN method is better than the model produced by the LSTM method.

Among the blood cells, white blood cells (WBC), which play a crucial role in forming our body's defense system, are essential components. Originating in the bone marrow, these cells serve as the fundamental components of the immune system, shouldering the responsibility of safeguarding the body against foreign microbes and diseases. Insufficient WBC counts may compromise the body's skill to resist infections, a status known as leukopenia. White blood cell counting is a specialty procedure that is usually carried out by qualified physicians and radiologists. Thanks to recent advances, image processing techniques are frequently used in biological systems to identify a wide spectrum of illnesses. In this work, image processing techniques were applied to enhance the white blood cell deep learning models' classification accuracy. To expedite the classification process, Convolutional Neural Network (CNN) models were combined with Ridge feature selection and Maximal Information Coefficient techniques. These tactics successfully determined the most important characteristics. The selected feature set was then applied to the classification procedure. ResNet-50, VGG19, and our suggested model were used as feature extractors in this study. The categorizing of white blood cells was completed with an amazing 98.27% success rate. Results from the experiments demonstrated a considerable improvement in classification accuracy using the proposed CNN model.

Sales forecasting is essential for businesses to predict future demand and inform strategic and operational planning, especially in the building materials retail industry. Accurate sales prediction supports inventory management, cost control, and supply chain efficiency. This study compares the performance of 3 deep learning models, Convolutional Neural Network (CNN), Multilayer Perceptron (MLP), and Long Short-Term Memory (LSTM), in forecasting daily iron sales at PT Surya Aneka Bangunan from 2016 to 2020. The models were trained on 80% of the historical data and tested on 20%. Model performance was evaluated using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Coefficient of Determination R². The results show that the CNN model achieved the best performance with an MAE of 0.293, RMSE of 0.357, MAPE of 0.081, and R² of 0.9989, indicating high accuracy and stability. The MLP model produced higher errors, while the LSTM model had the lowest MAPE but greater error variability. These findings suggest that the CNN model is the most reliable for capturing temporal patterns in iron sales data. The study contributes to the development of adaptive sales forecasting systems and opens opportunities for applying similar methods in other retail sectors to support data driven decision making.

This study leveraged Convolutional Neural Network (CNN) models to estimate canopy height in Southeast Australian forests before and after the 2019–2020 bushfire event, using inputs from Sentinel-1, Sentinel-2, spectral indices, and terrain features and GEDI canopy height (rh99) as target variables. Our research consisted of three primary objectives: (1) an evaluation of GEDI rh99 canopy height in relation to an nDSM derived from airborne LiDAR, (2) the development of two CNN models for pre- and post-fire scenarios, and (3) utilizing the trained CNN models to generate pre- and post-fire canopy height maps and canopy height change maps across the continuous landscape. GEDI rh99 accuracy analysis revealed R 2 = 0.85, RMSE = 7.25 m and nRMSE = 0.23 for pre-fire GEDI and R 2 = 0.78, RMSE = 10.1 m and nRMSE = 0.3 for post-fire GEDI. The pre-fire CNN model achieved R 2 = 0.75, RMSE = 7.29 m and nRMSE = 0.22, and post-fire CNN model achieved R 2 = 0.62, RMSE = 11.67 m, and nRMSE = 0.34 when validated against nDSM. Bias analysis was conducted, revealing that GEDI rh99 underestimated nDSM across all canopy height ranges, and slope has no significant impact on GEDI accuracy. Notably, post-fire GEDI rh99 exhibited significant underestimation in high to extreme fire severity. The CNN models’ prediction displayed overestimation in short forests < 20 m and increasing underestimation in taller forests > 20 m and underestimation in high and extreme fire severity. Subsequent investigation indicated that canopy layer thinning in high fire severity conditions resulted in weak GEDI waveform signals and consequent bias. Additionally, the GEDI rh99 bias was propagated to the CNN models, together with insensitivity of spectral band values to canopy height in dense and burnt forests potentially contributed to models’ bias. Finally, we demonstrated CNN models’ ability to monitor forest recovery by generating canopy height maps spanning East Gippsland from 2019 to 2023.

In this paper, we propose a method to analyze berg balance scale (BBS) for elderly persons using convolutional neural network (CNN). One of the main problem for elderly people is that their health is easily get deteriorated after a fall. The proposed CNN model predicts the BBS score of the elderly using sensors that can measure the motion pattern while performing activities of BBS. We developed two CNN models – one dimensional and two dimensional – to predict the score of BBS activities. The models predict the score between 0 to 4 with the accuracy of 90%. The proposed CNN models can be used to evaluate fall risk for elderly persons.

People counting and pedestrian flow statistics are challenging tasks because of the perspective distortions, appearance changes and occlusion. In this paper, we address the two tasks: people counting in images of highly dense crowds and pedestrian flow statistics in a place over a period of time. Our first contribution is to propose a new convolution neural network (CNN) model which is composed of a deep and shallow fully convolution network to fulfill the task of people counting. We extract different layer features from the deep fully convolution network and the last layer features from the shallow fully convolution network, and concatenate them together. After that we add two deconvolution layers to make the output image have the same resolution with the input image. Our second contribution is to combine pedestrian flow statistics task with people counting task. According to the density maps that CNN model generates, we can calculate the number of people crossing a place based on the recurrent neural network (RNN). Besides, we also have collected two datasets and labelled them. Extensive experiments have been implemented, our people counting method outperforms other existing methods, and our pedestrian flow statistics method combined with CNN model also outperforms the model which only uses long-short term memory (LSTM).

Distinguishing pericarpium citri reticulatae of different origins using terahertz time-domain spectroscopy combined with convolutional neural networks