Simple Convolutional Neural Network Research Articles

Methodology for the automated selection of time-frequency representations

Data preprocessing is a key step in extracting useful information from sound and vibration data and often involves selecting a time-frequency representation. No single time-frequency representation is always optimal, and no standard method exists for selecting the appropriate time-frequency representation, so selecting the time-frequency representation requires expert knowledge and is susceptible to human bias. To address this, this work introduces a methodology to automate the selection of a time-frequency representation for a dataset using only a subset of the healthy, or normal, class of data. To select the parameters for each type of time-frequency representation, Bayesian optimization is used. With a candidate from each type of time-frequency representation, the average similarity is used to select the final candidate. Additionally, the use of multiple time-frequency representations within a single model is explored. Because there is currently no objective method to compare the selected time frequency representations against, the proposed methodology is evaluated in two case studies. In the case studies, the time frequency representations are used as inputs to a simple convolutional neural network that achieved 100% accuracy in classifying bearing faults and 94% accuracy in classifying the contact tip to workpiece distance in wire arc additive manufacturing. Additionally, the proposed methodology presents a 75% and 94% reduction in the data size for the two case studies. This offers further benefits for reducing costs of data transmission and storage in modern digital manufacturing architectures.

Exploring Recent NLP Advances for Tamil: Word Vectors and Hybrid Deep Learning Architectures

The advancements of deep learning methods and the availability of large corpora and data sets have led to an exponential increase in the performance of Natural Language Processing (NLP) methods resulting in successful NLP applications for various day-to-day tasks such as Language translation, Voice to text, Grammar checking, Sentiment analysis, etc. These advancements enabled the well-resourced languages to adapt themselves to the digital era while the gap for the low-resource languages widened. This research work explores the suitability of the recent advancements in NLP for Tamil, a low-resource language spoken mainly in South India, Sri Lanka, and Malaysia. From the literature survey, it has been found that there is a lack of comprehensive study on the effect of the recent advancements of NLP for the Tamil text. To fill this gap, this research work analysed the performance of deep learning based text representation and classification approaches namely word embedding, Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN) for Tamil text classification tasks. Different dimensional pretrained word2Vec and FastText word vectors were built for Tamil and their effectiveness on Text classification was evaluated. The study found that the pre-trained 300- dimensional FastText word vector showed better performance than other pre-trained word vectors for Tamil text classification. Further, in this study, four simple hybrid CNN and Bi-GRU models were proposed for Tamil text classification and their performances were evaluated. The study found that hybrid CNN and Bi-GRU models perform better compared to the classical machine learning models, individual CNN and RNN models, and the Multilingual BERT model. These results confirm that the jointly learned embeddings with different deep learning architectures like CNN and RNN can achieve remarkable results for Tamil text classification, thus ensuring that the deep learning approaches can be successful for NLP on Tamil text.

Pigment network detection and classification in dermoscopic images using directional imaging algorithms and convolutional neural networks

Early diagnosis of melanoma, which can save thousands of lives, relies heavily on the analysis of dermoscopic images. One crucial diagnostic criterion is the identification of unusual pigment network (PN). However, distinguishing between regular (typical) and irregular (atypical) PN is challenging. This study aims to automate the PN detection process using a directional imaging algorithm and classify PN types using machine learning classifiers. The directional imaging algorithm incorporates Principal Component Analysis (PCA), contrast enhancement, filtering, and noise reduction. Applied to the PH2 dataset, this algorithm achieved a 96% success rate, which increased to 100% after pixel intensity adjustments. We created a new dataset containing only PN images from these results. We then employed two classifiers, Convolutional Neural Network (CNN) and Bag of Features (BoF), to categorize PN into atypical and typical classes. Given the limited dataset of 200 images, a simple and effective CNN was designed, featuring two convolutional layers and two batch normalization layers. The proposed CNN achieved 90% accuracy, 90% sensitivity, and 89% specificity. When compared to state-of-the-art methods, our CNN demonstrated superior performance. Our study highlights the potential of the proposed CNN model for effective PN classification, suggesting future research should focus on expanding datasets and incorporating additional dermatological features to further enhance melanoma diagnosis.

Accurate deep learning-based filtering for chaotic dynamics by identifying instabilities without an ensemble.

We investigate the ability to discover data assimilation (DA) schemes meant for chaotic dynamics with deep learning. The focus is on learning the analysis step of sequential DA, from state trajectories and their observations, using a simple residual convolutional neural network, while assuming the dynamics to be known. Experiments are performed with the Lorenz 96 dynamics, which display spatiotemporal chaos and for which solid benchmarks for DA performance exist. The accuracy of the states obtained from the learned analysis approaches that of the best possibly tuned ensemble Kalman filter and is far better than that of variational DA alternatives. Critically, this can be achieved while propagating even just a single state in the forecast step. We investigate the reason for achieving ensemble filtering accuracy without an ensemble. We diagnose that the analysis scheme actually identifies key dynamical perturbations, mildly aligned with the unstable subspace, from the forecast state alone, without any ensemble-based covariances representation. This reveals that the analysis scheme has learned some multiplicative ergodic theorem associated to the DA process seen as a non-autonomous random dynamical system.

Analyzing the heterogenous effects of factors on high-range speeding likelihood of taxi speeders: Does explainable deep learning provides more insights than random parameter approach?

The random parameters Generalized Linear Model (GLM) is frequently used to model speeding characteristics and capture the heterogenous effects of factors. However, this statistical approach is seldom employed for prediction and generalization due to the challenge of transferring its predefined errors. Recently, the emergence of explainable AI techniques has illuminated a new path for analyzing factors associated with risky driving behaviors. Despite this, there remains a gap that comparing results from machine and deep learning (ML/DL) approaches with those from random parameters GLM. This study aims to apply the random parameter GLM and explainable deep learning to evaluate the heterogenous effects of factors on the taxis’ high-range speeding likelihood. Initially, a Beta GLM with random parameters (BGLM-RP) is developed to model the high-range speeding likelihood among taxi drivers. Additionally, XGBoost, a simple convolutional neural network (Simple-CNN), a deeper CNN (DCNN), and a deeper CNN with self-attention (DCNN-SA) are developed. The quantified explanations and illustrations of the factors’ heterogenous effects from ML/DL models are derived from pseudo coefficients by decomposing factors’ SHapley Additive exPlanations (SHAP) values. All the developed statistical, ML, and DL models are compared in terms of mean absolute errors and mean square errors on testing and full data. Results show that DCNN-SA excels in prediction on testing data, indicating its superior generalization capabilities, while BGLM-RP outperforms other models on full data. The DCNN-SA can reveal the heterogenous effects of factors for both in-sample and out-of-sample data, which is not possible for the random parameter GLM. However, BGLM-RP can reveal larger magnitudes of the factors’ heterogenous effects for in-sample data. The signs and significances are identical between the varying coefficients from BGLM-RP and the pseudo coefficients from the ML/DL models, demonstrating the validity and rationale of using the proposed explanation framework to quantify the factors’ effects in ML/DL models. The study also discusses the contributions of various factors to the high-range speeding likelihood of taxi drivers.

Deep neural networks as variational solutions for correlated open quantum systems

In this work we apply deep neural networks to find the non-equilibrium steady state solution to correlated open quantum many-body systems. Motivated by the ongoing search to find more powerful representations of (mixed) quantum states, we design a simple prototypical convolutional neural network and show that parametrizing the density matrix directly with more powerful models can yield better variational ansatz functions and improve upon results reached by neural density operator based on the restricted Boltzmann machine. Hereby we give up the explicit restriction to positive semi-definite density matrices. However, this is fulfilled again to good approximation by optimizing the parameters. The great advantage of this approach is that it opens up the possibility of exploring more complex network architectures that can be tailored to specific physical properties. We show how translation invariance can be enforced effortlessly and reach better results with fewer parameters. We present results for the dissipative one-dimensional transverse-field Ising model and a two-dimensional dissipative Heisenberg model compared to exact values.

LightNet: A Novel Lightweight Convolutional Network for Brain Tumor Segmentation in Healthcare.

Diagnosis, treatment planning, surveillance, and the monitoring of clinical trials for brain diseases all benefit greatly from neuroimaging-based tumor segmentation. Recently, Convolutional Neural Networks (CNNs) have demonstrated promising results in enhancing the efficiency of image-based brain tumor segmentation. Most current work on CNNs, however, is devoted to creating increasingly complicated convolution modules to improve performance, which in turn raises the computing cost of the model. This work proposes a simple and effective feed-forward CNN, LightNet (Light Network). Based on multi-path and multi-level, it replaces traditional convolutional methods with light operations, which reduces network parameters and redundant feature maps. In the up-sampling stage, a light channel attention module is added to achieve richer multi-scale and spatial semantic feature information extraction of brain tumor. The performance of the network is evaluated in the Multimodal Brain Tumor Segmentation Challenge (BraTS 2015) dataset, and results are presented here alongside other high-performing CNNs. Results show comparable accuracy with other methods but with increased efficiency, segmentation performance, and reduced redundancy and computational complexity. The result is a high-performing network with a balance between efficiency and accuracy, allowing, for example, better energy performance on mobile devices.

Analyzing deep learning algorithms with statistical methods

A number of farmers use human inspection to classify the quality of their fruits visually; this process could be done automatically with a Learning algorithm. This paper investigates the classification of apples, peaches, and oranges and verifies the quality of classification with the density of the edges detected in each image by the Sobel filter. The novelty of the study consists of comparing the classification accuracy of a convolutional neural network (CNN) and the statistical analysis of edge histograms. The edges are the main elements of what the CNN learns; this was the reason for verifying the deep learning algorithms with skewness, kurtosis, and standard deviation metrics extracted from the histogram of edge density detected by the Sobel filter. In the empirical process, four algorithms were proposed: a CNN with one convolutional layer, a DNN without convolutional layers, and two pre-trained networks, AlexNet and GoogLeNet. From clustering of the row data and analyzing the accuracy, the best classification is obtained by simple CNN for 20 epochs when the apples vs. oranges were classified.

3D positioning of Camellia oleifera fruit-grabbing points for robotic harvesting

Camellia oleifera is an oilseed crop with high economic value. The short optimum harvest period and high labour costs of C. oleifera harvesting have prompted research on intelligent robotic harvesting. This study focused on the determination of grabbing points for the robotic harvesting of C. oleifera fruits, providing a basis for the decision making of the fruit-picking robot. A relatively simple 2D convolutional neural network (CNN) and stereoscopic vision replaced the complex 3D CNN to realise the 3D positioning of the fruit. Apple datasets were used for the pretraining of the model and knowledge transfer, which shared a certain degree of similarity to C. oleifera fruit. In addition, a fully automatic coordinate conversion method has been proposed to transform the fruit position information in the image into its 3D position in the robot coordinate system. Results showed that the You Only Look Once (YOLO)v8x model trained using 1012 annotated samples achieved the highest performance for fruit detection, with mAP50 of 0.96 on the testing dataset. With knowledge transfer based on the apple datasets, YOLOv8x using few-shot learning realised a testing mAP50 of 0.95, reducing manual annotation. Moreover, the error in the 3D coordinate calculation was lower than 2.1 cm on the three axes. The proposed method provides the 3D coordinates of the grabbing point for the target fruit in the robot coordinate system, which can be transferred directly to the robot control system to execute fruit-picking actions. This dataset was published online to reproduce the results of this study.

Sugarcane Diseases Detection Using Optimized Convolutional Neural Network with Enhanced Environmental Adaptation Method

This research aims to address the need for accurate and prompt identification of sugarcane diseases, which substantially impact the worldwide sugar industry and the livelihoods of numerous farmers. Conventional visual inspection methods are hindered by subjective interpretations and restricted availability, prompting the investigation of more sophisticated techniques. By harnessing deep learning capabilities, specifically Convolutional Neural Networks (CNNs), and further enhancing their performance using the Environmental Adaptation Method (EAM) optimization, this research demonstrates significant enhancements in disease detection accuracy, precision, recall, and F1-Score. Based on the macro values obtained from the different approaches, it has been observed that an accuracy of 89% was obtained for the CNN designed from EEAM in comparison to the other counter parts. Similarly, the precision of the proposed architecture of CNN is better in comparison to GA, PSO and DE. On the same lines the Recall and F1 score of the proposed approach is better in comparison to that of the three counterparts. Similarly, the ROC analysis for the analysis of AUC is done and it was identified that the AUC curve for the different CNN designed by various optimizer were good in identifying the different classes of the sugarcane diseases. The major limitation of this approach is that model has marginal accuracy with its counterpart algorithm, however, the algorithm suggested the use of simple CNN models that are easy to use. The rigorous methodology, encompassing data collection and model optimization, guarantees the reliability and applicability of the sugarcane disease detection system based on Convolutional Neural Networks (CNN). Future research directions focus on integrating hyperspectral imaging, unmanned aerial vehicles (UAVs), and user-friendly mobile applications. This integration aims to empower farmers, facilitate proactive disease management, and ensure the sustainability of the sugarcane industry. This advancement represents notable progress in precision agriculture and disease mitigation.

Black phosphorus quantum dots functionalized with photochromic poly(vinylspiropyran)-grafted polydopamine for transient digital-type memristors

Abstract The covalent functionalization of black phosphorus quantum dots (BPQDs) with organic species or polymers will inevitably change or damage their electronic structure and intrinsic structure. To address this problem and explore the application of BPQDs in transient digital-type memristors, a polydopamine (PDA) thin film is first synthesized in situ onto the surface of BPQDs to produce a donor–acceptor-type BPQDs@PDA composite that is directly used to react with 2-bromoisobutyryl bromide to give BPQDs@PDA-Br. By using BPQDs@PDA-Br as an atom transfer radical polymerization agent, a large number of polyvinylspiropyran (PSP) chains are in situ grown from the PDA surface to yield BPQDs@PDA-PSP. Upon ultraviolet (UV)–visible light illumination, the 2 isomers of the spiropyran (ring-closed spiropyran form and ring-opened merocyanine) in the PSP moieties will interconvert into each other rapidly. As expected, the as-fabricated indium tin oxide (ITO)/BPQDs@PDA-PSP/ITO device exhibits typical nonvolatile digital-type memristive performance under visible irradiation, with a small turn-on voltage of −1.52 V, a turn-off voltage of +1.16 V, and an ON/OFF ratio current ratio of 1.02 × 104. Upon UV illumination, the information stored in the device is quickly and completely erased within 6 s. By utilizing a simple memristor-based convolutional neural network, one can easily realize handwritten digit recognition. After 10 epochs of training, numeral recognition accuracy can reach up to 96.21%.

Deep learning with convolution neural network detecting mesiodens on panoramic radiographs: comparing four models.

The aim of this study was to develop an optimal, simple, and lightweight deep learning convolutional neural network (CNN) model to detect the presence of mesiodens on panoramic radiographs. A total of 628 panoramic radiographs with and without mesiodens were used as training, validation, and test data. The training, validation, and test dataset were consisted of 218, 51, and 40 images with mesiodens and 203, 55, and 61 without mesiodens, respectively. Unclear panoramic radiographs for which the diagnosis could not be accurately determined and other modalities were required for the final diagnosis were retrospectively identified and employed as the training dataset. Four CNN models provided within software supporting the creation of neural network models for deep learning were modified and developed. The diagnostic performance of the CNNs was evaluated according to accuracy, precision, recall and F1 scores, receiver operating characteristics (ROC) curves, and area under the ROC curve (AUC). In addition, we used SHapley Additive exPlanations (SHAP) to attempt to visualize the image features that were important in the classifications of the model that exhibited the best diagnostic performance. A binary_connect_mnist_LeNet model exhibited the best performance of the four deep learning models. Our results suggest that a simple lightweight model is able to detect mesiodens. It is worth referring to AI-based diagnosis before an additional radiological examination when diagnosis of mesiodens cannot be made on unclear images. However, further revaluation by the specialist would be also necessary for careful consideration because children are more radiosensitive than adults.

Comparative analysis of potato blight diseases BARI-72 and BARI-73 using a simplified convolutional neural network method

Comparative analysis of potato blight diseases BARI-72 and BARI-73 using a simplified convolutional neural network method

Spatiotemporal self-supervised predictive learning for atmospheric variable prediction via multi-group multi-attention

Atmospheric variable prediction (AVP) is crucial for atmospheric and environmental science, as well as practical applications related to production and daily life. Traditional AVP commonly relies on numerical modeling methods. In recent years, deep learning (DL) technologies have introduced new approaches for AVP. With the support of massive high-quality data products, DL methods have achieved remarkable progress. Simultaneously, studying and annotating complex phenomena and processes within vast datasets require specialized researchers and come with substantial costs. The self-supervised learning (SSL) mechanism in DL can facilitate representation learning from unlabeled data, thereby significantly reducing the expenses associated with manual annotation. However, there has been limited exploration of the SSL mechanism in atmospheric variable prediction and analysis. Furthermore, the prevailing large-scale predicting applications based on AVP mainly focus on massive data processing, multi-modal approaches, and foundation model technologies, often neglecting the critical aspects of spatiotemporal representation learning. In light of the above, based on the cutting-edge DL techniques but diverges from the current popular contrastive or generative SSL frameworks, we devise a spatiotemporal (ST) self-supervised predictive learning (SSPL) method (dubbed ST-SSPL) for AVP, which enables the learning of predictive characteristics from unlabeled atmospheric data. Additionally, we improve a multi-group multi-attention (MGMA) method, integrating it into our predictive framework to enhance spatiotemporal representation learning. In experiments conducted on the widely used ERA5 dataset, the proposed ST-SSPL method has achieved superior performance and high efficiency using a simple convolutional neural network (CNN) architecture. These advancements offer crucial methods and references for relevant large-scale applications.

Histological tissue classification with a novel statistical filter-based convolutional neural network.

Deep networks have been of considerable interest in literature and have enabled the solution of recent real-world applications. Due to filters that offer feature extraction, Convolutional Neural Network (CNN) is recognized as an accurate, efficient and trustworthy deep learning technique for the solution of image-based challenges. The high-performing CNNs are computationally demanding even if they produce good results in a variety of applications. This is because a large number of parameters limit their ability to be reused on central processing units with low performance. To address these limitations, we suggest a novel statistical filter-based CNN (HistStatCNN) for image classification. The convolution kernels of the designed CNN model were initialized by continuous statistical methods. The performance of the proposed filter initialization approach was evaluated on a novel histological dataset and various histopathological benchmark datasets. To prove the efficiency of statistical filters, three unique parameter sets and a mixed parameter set of statistical filters were applied to the designed CNN model for the classification task. According to the results, the accuracy of GoogleNet, ResNet18, ResNet50 and ResNet101 models were 85.56%, 85.24%, 83.59% and 83.79%, respectively. The accuracy was improved by 87.13% by HistStatCNN for the histological data classification task. Moreover, the performance of the proposed filter generation approach was proved by testing on various histopathological benchmark datasets, increasing average accuracy rates. Experimental results validate that the proposed statistical filters enhance the performance of the network with more simple CNN models.

DBGAN: Dual Branch Generative Adversarial Network for Multi-Modal MRI Translation

Existing magnetic resonance imaging translation models rely on generative adversarial networks, primarily employing simple convolutional neural networks. Unfortunately, these networks struggle to capture global representations and contextual relationships within magnetic resonance images. While the advent of Transformers enables capturing long-range feature dependencies, they often compromise the preservation of local feature details. To address these limitations and enhance both local and global representations, we introduce DBGAN , a novel dual-branch generative adversarial network. In this framework, the Transformer branch comprises sparse attention blocks and dense self-attention blocks, allowing for a wider receptive field while simultaneously capturing local and global information. The convolutional neural network branch, built with integrated residual convolutional layers, enhances local modeling capabilities. Additionally, we propose a fusion module that cleverly integrates features extracted from both branches. Extensive experimentation on two public datasets and one clinical dataset validates significant performance improvements with DBGAN. On Brats2018, it achieves a 10% improvement in MAE, 3.2% in PSNR, and 4.8% in SSIM for image generation tasks compared to RegGAN. Notably, the generated MRIs receive positive feedback from radiologists, underscoring the potential of our proposed method as a valuable tool in clinical settings.

Test Platform for Developing New Optical Position Tracking Technology towards Improved Head Motion Correction in Magnetic Resonance Imaging.

Optical tracking of head pose via fiducial markers has been proven to enable effective correction of motion artifacts in the brain during magnetic resonance imaging but remains difficult to implement in the clinic due to lengthy calibration and set up times. Advances in deep learning for markerless head pose estimation have yet to be applied to this problem because of the sub-millimetre spatial resolution required for motion correction. In the present work, two optical tracking systems are described for the development and training of a neural network: one marker-based system (a testing platform for measuring ground truth head pose) with high tracking fidelity to act as the training labels, and one markerless deep-learning-based system using images of the markerless head as input to the network. The markerless system has the potential to overcome issues of marker occlusion, insufficient rigid attachment of the marker, lengthy calibration times, and unequal performance across degrees of freedom (DOF), all of which hamper the adoption of marker-based solutions in the clinic. Detail is provided on the development of a custom moiré-enhanced fiducial marker for use as ground truth and on the calibration procedure for both optical tracking systems. Additionally, the development of a synthetic head pose dataset is described for the proof of concept and initial pre-training of a simple convolutional neural network. Results indicate that the ground truth system has been sufficiently calibrated and can track head pose with an error of <1 mm and <1°. Tracking data of a healthy, adult participant are shown. Pre-training results show that the average root-mean-squared error across the 6 DOF is 0.13 and 0.36 (mm or degrees) on a head model included and excluded from the training dataset, respectively. Overall, this work indicates excellent feasibility of the deep-learning-based approach and will enable future work in training and testing on a real dataset in the MRI environment.

Convolutional and hybrid neural network for cluster membership

We conduct investigations of the membership of galaxies inside a cluster using a machine-learning Convolutional Neural Network (CNN), and a hybrid of CNN with Multi-Layer Perceptron (MLP) neural network. These networks are trained using optical images of each galaxy in I and V bands as well as using various photometric and morphological parameters of galaxies extracted and measured from these images. The optical images are observed with Subaru Suprime-Cam for 16 clusters at redshift ∼ 0.15–0.3. This dataset enables a wide and faint investigation of cluster membership that is essential for the investigations of dwarf galaxies, but it is often difficult to achieve both simultaneously. We optimize the network structure to directly determine the membership classification, rather than estimating the galaxy redshift first as an intermediate step and then determining the membership from the redshifts. We find: 1. The CNN model can be useful for the application of identifying the cluster membership of galaxies, similar to the MLP model, implying that the CNN model can use information beyond galaxy SED. 2. A simple CNN model appears to predict the cluster membership less accurately than the 17-measure MLP model. 3. For better performance of CNN, the masking of the object is important, as well as scaling based on the photometric calibration. 4. The hybrid model of CNN and MLP combined can produce a better prediction than the single MLP (or CNN) model; however, the improvement is small at best implying that the information extracted by 17 measures for the MLP model is fairly comprehensive in sense of the total information contained in pixels of the image. 5. All models appear to predict the membership more accurately inside a smaller radius closer to the cluster center. 6. Faint galaxies are harder to assign their memberships even if using our models, though the performance is more robust than other photometric methods.Artificial intelligence (AI) can help us effectively combine various conventional methods of finding cluster membership. By making it inherit advantages of each of these conventional methods, The AI membership can achieve overall good performance in various performance statistics simultaneously, such as purity, completeness, F1 score, AUC, and accuracy. The overall good performance of the AI membership is vital to cluster studies in the era of faint and data-intensive galaxy survey s in which the complete spectroscopic observation of all galaxies is out of reach.

TSSG-CNN: A Tuberculosis Semantic Segmentation-Guided Model for Detecting and Diagnosis Using the Adaptive Convolutional Neural Network.

Tuberculosis (TB) is an infectious disease caused by Mycobacterium. It primarily impacts the lungs but can also endanger other organs, such as the renal system, spine, and brain. When an infected individual sneezes, coughs, or speaks, the virus can spread through the air, which contributes to its high contagiousness. The goal is to enhance detection recognition with an X-ray image dataset. This paper proposed a novel approach, named the Tuberculosis Segmentation-Guided Diagnosis Model (TSSG-CNN) for Detecting Tuberculosis, using a combined semantic segmentation and adaptive convolutional neural network (CNN) architecture. The proposed approach is distinguished from most of the previously proposed approaches in that it uses the combination of a deep learning segmentation model with a follow-up classification model based on CNN layers to segment chest X-ray images more precisely as well as to improve the diagnosis of TB. It contrasts with other approaches like ILCM, which is optimized for sequential learning, and explainable AI approaches, which focus on explanations. Moreover, our model is beneficial for the simplified procedure of feature optimization from the perspectives of approach using the Mayfly Algorithm (MA). Other models, including simple CNN, Batch Normalized CNN (BN-CNN), and Dense CNN (DCNN), are also evaluated on this dataset to evaluate the effectiveness of the proposed approach. The performance of the TSSG-CNN model outperformed all the models with an impressive accuracy of 98.75% and an F1 score of 98.70%. The evaluation findings demonstrate how well the deep learning segmentation model works and the potential for further research. The results suggest that this is the most accurate strategy and highlight the potential of the TSSG-CNN Model as a useful technique for precise and early diagnosis of TB.

A good result of brain tumor classification based on simple convolutional neural network architecture

A good result of brain tumor classification based on simple convolutional neural network architecture