Deep Convolutional Neural Network Model Research Articles

Transfer learning with fine-tuned deep CNN model for COVID-19 diagnosis from chest X-ray images

Transfer learning with fine-tuned deep CNN model for COVID-19 diagnosis from chest X-ray images

Artificial intelligence for evaluating the risk of gastric cancer: reliable detection and scoring of intestinal metaplasia with deep learning algorithms

Gastric cancer (GC) is associated with chronic gastritis. To evaluate the risk, the Operative Link on Gastric Intestinal Metaplasia Assessment (OLGIM) system was constructed and showed a higher GC risk in stage III or IV patients, determined by the degree of intestinal metaplasia (IM). While the OLGIM system is useful, evaluating the degree of IM requires substantial experience to produce precise scoring. Whole-slide imaging is becoming routine, but most artificial intelligence (AI) systems in pathology are focused on neoplastic lesions. Hematoxylin and eosin-stained slides were scanned. Images were divided into each gastric biopsy tissue and labelled with an IM score. IM was scored as follows: 0 (no IM), 1 (mild IM), 2 (moderate IM), and 3 (severe IM). Overall, 5,753 images were prepared. A deep convolutional neural network (DCNN) model, ResNet50, was used for classification. ResNet50 classified images with and without IM with a sensitivity of 97.7% and specificity of 94.6%. IM scores 2 and 3, those are involved as criteria of stage III or IV in the OLGIM system were classified by ResNet50 in 18 %. The respective sensitivity and specificity values of classifying IM between scores 0, 1, and 2, 3 were 98.5% and 94.9%. The IM scores classified by pathologists and the AI system were different in only 438 (7.6%) of all images, and we found that ResNet50 tended to miss small foci of IM, while it successfully identified minimal IM areas that pathologists missed during the review. Our findings suggested that this AI system would contribute to evaluating the risk of gastric cancer accuracy, reliability, and repeatability with worldwide standardization.

Early Identification of Root Damages Caused by Western Corn Rootworms Using a Minimally Invasive Root Phenotyping Robot-MISIRoot.

Western corn rootworm (WCR) is one of the most devastating corn rootworm species in North America because of its ability to cause severe production loss and grain quality damage. To control the loss, it is important to identify the infection of WCR at an early stage. Because the root system is the earliest feeding source of the WCR at the larvae stage, assessing the direct damage in the root system is crucial to achieving early detection. Most of the current methods still necessitate uprooting the entire plant, which could cause permanent destruction and a loss of the original root's structural information. To measure the root damages caused by WCR non-destructively, this study utilized MISIRoot, a minimally invasive and in situ automatic plant root phenotyping robot to collect not only high-resolution images but also 3D positions of the roots without uprooting. To identify roots in the images and to study how the damages were distributed in different types of roots, a deep convolution neural network model was trained to differentiate the relatively thick and thin roots. In addition, a color camera was used to capture the above-ground morphological features, such as the leaf color, plant height, and side-view leaf area. To check if the plant shoot had any visible symptoms in the inoculated group compared to the control group, several vegetation indices were calculated based on the RGB color. Additionally, the shoot morphological features were fed into a PLS-DA model to differentiate the two groups. Results showed that none of the above-ground features or models output a statistically significant difference between the two groups at the 95% confidence level. On the contrary, many of the root structural features measured using MISIRoot could successfully differentiate the two groups with the smallest t-test p-value of 1.5791 × 10-6. The promising outcomes were solid proof of the effectiveness of MISIRoot as a potential solution for identifying WCR infestations before the plant shoot showed significant symptoms.

A novel deep learning model with the Grey Wolf Optimization algorithm for cotton disease detection

 Plants are a big part of the ecosystem. Plants are also used by humans for various purposes. Cotton is one of these important plants and is very critical for humans. Cotton production is one of the most important sources of income for many countries and farmers in the world. Cotton can get diseases like other plants and living things. Detecting these diseases is critical. In this study, a model is developed for disease detection from leaves of cotton. This model determines whether the cotton is healthy or diseased through the photograph. It is a deep convolutional neural network model. While establishing the model, care is taken to ensure that it is a problem-specific model. The grey wolf optimization algorithm is used to ensure that the model architecture is optimal. So, this algorithm will find the most efficient architecture. The proposed model has been compared with the ResNet50, VGG19, and InceptionV3 models that are frequently used in the literature. According to the results obtained, the proposed model has an accuracy value of 1.0. Other models had accuracy values of 0.726, 0.934, and 0.943, respectively. The proposed model is more successful than other models. 

Editorial

Dear Readers,  It gives me great pleasure to announce the sixth regular issue of 2023. In this issue, 5 papers cover various topical aspects of computer science by 16 authors from 4 countries. In an ongoing effort to further strengthen our journal, I would like to expand the editorial board: If you are a tenured associate professor or above with a strong publication record, you are welcome to apply to join our editorial board. We are also interested in high-quality proposals for special issues on new topics and trends.  As always, I would like to thank all authors for their sound research and the editorial board and our guest reviewers for their extremely valuable review effort and suggestions for improvement. These contributions, together with the generous support of the consortium members, sustain the quality of our journal.  In this regular issue, I am very pleased to introduce the following 5 accepted articles: In a collaborative research effort between Morocco and Spain, Soukaina Benabdelouahab, José A. García-Berná, Chaimae Moumouh, Juan M. Carrillo-de-Gea, Jaber El Bouhdidi, Yacine El Younoussi, and José L. Fernández-Alemán report on their bibliometric study on e-learning software engineering education by conducting an analysis of 10,603 publications, dating from 1954 to 2020. Achraf Boumhidi, Abdessamad Benlahbib and El Habib Nfaoui from Morocco propose in their research a financially-oriented reputation system that generates a single numerical value from user-generated content on Twitter toward cryptocurrencies by applying a sentiment polarity extractor based on the fine-tuned auto-regressive language model named XLNet. Samuel Caetano da Silva and Ivandré Paraboni from Brazil focus on politically-oriented information inference from text by a series of experiments to compare a number of strategies for political bias and ideology inference from text data using sequence-based BERT models, syntax- and semantics-driven features. Burak Gülmez from Türkiye discusses a model developed for disease detection from images of cotton leaves applying a deep convolutional neural network model and the grey wolf optimization algorithm. This model outperforms the ResNet50, VGG19, and InceptionV3 models frequently used in the literature. Last but not least, José L. López Ruiz, Ángeles Verdejo Espinosa and Macarena Espinilla Estévez from Spain discuss a new methodology for the optimization of Bluetooth anchors for location-relevant systems in an enclosed space using a machine learning-based inference model based on different configurations of the BLE anchors located in the enclosed environment.  Enjoy Reading!  Cordially,  Christian Gütl, Managing Editor

A deep convolutional neural network for salt-and-pepper noise removal using selective convolutional blocks

In recent years, there has been an unprecedented upsurge in applying deep learning approaches, specifically convolutional neural networks (CNNs), to solve image denoising problems, owing to their superior performance. However, CNNs mostly rely on Gaussian noise, and there is a conspicuous lack of exploiting CNNs for salt-and-pepper (SAP) noise reduction. In this paper, we proposed a deep CNN model, namely SeConvNet, to suppress SAP noise in gray-scale and color images. To meet this objective, we introduce a new selective convolutional (SeConv) block. SeConvNet is compared to state-of-the-art SAP denoising methods using extensive experiments on various common datasets. The results illustrate that the proposed SeConvNet model effectively restores images corrupted by SAP noise and surpasses all its counterparts at both quantitative criteria and visual effects, especially at high and very high noise densities.

Delensing of Cosmic Microwave Background Polarization with Machine Learning

Primordial B-mode detection is one of the main goals of next-generation cosmic microwave background (CMB) experiments. Primordial B-modes are a unique signature of primordial gravitational waves (PGWs). However, the gravitational interaction of CMB photons with large-scale structures will distort the primordial E modes, adding a lensing B-mode component to the primordial B-mode signal. Removing the lensing effect (“delensing”) from observed CMB polarization maps will be necessary to improve the constraint of PGWs and obtain a primordial E-mode signal. Here, we introduce a deep convolutional neural network model named multi-input multi-output U-net (MIMO-UNet) to perform CMB delensing. The networks are trained on simulated CMB maps with size 20° × 20°. We first use MIMO-UNet to reconstruct the unlensing CMB polarization (Q and U) maps from observed CMB maps. The recovered E-mode power spectrum exhibits excellent agreement with the primordial EE power spectrum. The recovery of the primordial B-mode power spectrum for noise levels of 0, 1, and 2 μK-arcmin is greater than 98% at the angular scale of ℓ < 150. We additionally reconstruct the lensing B map from observed CMB maps. The recovery of the lensing B-mode power spectrum is greater than roughly 99% at the scales of ℓ > 200. We delens the observed B-mode power spectrum by subtracting the reconstructed lensing B-mode spectrum. The recovery of tensor B-mode power spectrum for noise levels of 0, 1, and 2 μK-arcmin is greater than 98% at the angular scales of ℓ < 120. Even at ℓ = 160, the recovery of tensor B-mode power spectrum is still around 71%.

Transfer Learning-Based Osteoporosis Classification Using Simple Radiographs

Osteoporosis is a condition that affects the entire skeletal system, resulting in decreased density of bone mass and the weakening of bone tissue's micro-architecture. This leads to weaker bones that are more susceptible to fractures. Detecting and measuring bone mineral density has always been a critical area of focus for researchers in the diagnosis of bone diseases such as osteoporosis. However, existing algorithms used for osteoporosis diagnosis encounter challenges in obtaining accurate results due to X-ray image noise and variations in bone shapes, especially in low contrast conditions. Therefore, the development of efficient algorithms that can mitigate these challenges and improve the accuracy of osteoporosis diagnosis is essential. In this research paper, a comparative analysis was conducted Assessing the accuracy and efficiency of the latest deep learning CNN model, such as VGG16, VGG19, DenseNet121, Resnet50 and InceptionV3 in detecting to Classify Normal and Osteoporosis cases. The study employed 830 X-ray images of Spine, Hand, Leg, Knee, and Hip, comprising of Normal (420) and Osteoporosis (410) cases. Various performance metrics were utilized to evaluate each model, and the findings indicate that DenseNet121 exhibited superior performance with an accuracy rate of 93.4% with Achieving an error rate of 0.07 and a validation loss of only 0.57 in comparison with other models considered in this study.

Klasifikasi Penyakit Tanaman Cabai Rawit Dilengkapi Dengan Segmentasi Citra Daun dan Buah Menggunakan Yolo v7

Diseases that attack chili plants can be diagnosed early by observing symptoms or changes that occur in the leaves and fruit of the chili plant. However, diseases or pests that attack chili plants within a single plant can vary. In this study, YOLO v7 was used to perform leaf and chili segmentation on images, and the segmented results were then classified for chili plant disease using Deep Convolutional Neural Network (DCNN) Transfer Learning with the Fine Tuning method. The test results of the constructed model showed that the Yolo v7 segmentation accuracy was 0.970 on mAP50 when performing chili plant leaf and fruit segmentation. For the DCNN model testing with transfer learning method using the EfficientNetV2M based model, an accuracy value of 0.912 was obtained for leaf disease classification and an accuracy of 0.889 was obtained for chili fruit classification.&#x0D; Keyword: Chili Plant Diseases; Classification; Transfer Learning, Yolo v7 segmentation

Deep Gaussian convolutional neural network model in classification of cassava diseases using spectral data

Deep Gaussian convolutional neural network model in classification of cassava diseases using spectral data

Detecting the location of lung cancer on thoracoscopic images using deep convolutional neural networks.

The prevalence of minimally invasive surgeries has increased the need for tumor detection using thoracoscopic images during lung cancer surgery. We conducted this study to analyze the efficacy of a deep convolutional neural network (DCNN) for tumor detection using recorded thoracoscopic images of pulmonary surfaces. We collected 644 intraoperative thoracoscopic images of changes in pulmonary appearance from 427 patients with lung cancer between 2012 and 2021. The lesion areas on the thoracoscopic images were detected by bounding boxes using an advanced version of YOLO, a well-known DCNN for object detection. The DCNN model was trained and evaluated by a 15-fold cross-validation scheme. Each predicted bounding box was considered successful detection when it overlapped more than 50% of the lesion areas annotated by board-certified surgeons. Precision, recall, and F1-measured values of 91.9%, 90.5%, and 91.1%, respectively, were obtained. The presence of lymphatic vessel invasion was associated with successful detection (p = 0.045). The presence of pathological pleural invasion also showed a tendency toward successful detection (p = 0.081). The proposed DCNN-based algorithm yielded an accuracy of more than 90% tumor detection. These algorithms will help surgeons detect lung cancer displayed on a screen automatically.

Deep convolutional neural network based on CT images of pulmonary nodules in the lungs of adolescent and young adult patients with osteosarcoma.

The aim of the present study was to explore the diagnostic value of a deep convolutional neural network (DCNN) model for the diagnosis of pulmonary nodules in adolescent and young adult patients with osteosarcoma. For the present study, 675 chest CT images were retrospectively collected from 109 patients with clinically confirmed osteosarcoma who underwent chest CT examination at Hangzhou Third People's Hospital (Hangzhou, China) from March 2011 to February 2022. CT images were then evaluated using the DCNN and manual models. Subsequently, pulmonary nodules of osteosarcoma were divided into calcified nodules, solid nodules, partially solid nodules and ground glass nodules using the DCNN model. Those patients with osteosarcoma who were diagnosed and treated were followed up to observe dynamic changes in the pulmonary nodules. A total of 3,087 nodules were detected, while 278 nodules were missed compared with those determined using the reference standard given by the consensus of three Experienced radiologists., which was analyzed by two diagnostic radiologists. In the manual model group, 2,442 nodules were detected, while 657 nodules were missed. The DCNN model showed significantly higher sensitivity and specificity compared with the manual model (sensitivity, 0.923 vs. 0.908; specificity, 0.552 vs. 0.351; P<0.05). In addition, the DCNN model yielded an area under the curve (AUC) value of 0.795 [95% confidence interval (CI), 0.743-0.846], outperforming that of the manual model (AUC, 0.687; 95% CI, 0.629-0.732; P<0.05). The film reading time of the DCNN model was also significantly shorter compared with that of the manual model [mean ± standard deviation (SD); 173.25±24.10 vs. 328.32±22.72 sec; P<0.05)]. The AUC of calcified nodules, solid nodules, partially solid nodules and ground glass nodules was calculated to be 0.766, 0.771, 0.761 and 0.796, respectively, using the DCNN model. Using this model, the majority of the pulmonary nodules were detected in patients with osteosarcoma at the initial diagnosis (69/109, 62.3%), and the majority of these were found with multiple pulmonary nodules instead of a single nodule (71/109, 65.1% vs. 38/109, 34.9%). These data suggest that, compared with the manual model, the DCNN model proved to be beneficial for the detection of pulmonary nodules in adolescent and young adult patients with osteosarcoma, which may reduce the time of artificial radiograph reading. In conclusion, the proposed DCNN model, developed using data from 675 chest CT images retrospectively collected from 109 patients with clinically confirmed osteosarcoma, may be used as an effective tool to evaluate pulmonary nodules in patients with osteosarcoma.

Using a deep learning neural network for the identification of malignant cells in effusion cytology material.

To evaluate the application of an artificial neural network in the detection of malignant cells in effusion samples. In this retrospective study, we selected 90 cases of effusion cytology samples over 2 years. There were 52 cases of metastatic adenocarcinoma and 38 benign effusion samples. In each case, an average of five microphotographs from the representative areas were taken at 40× magnification from Papanicolaou-stained samples. A total of 492 images were obtained from these 90 cases. We applied a deep convolutional neural network (DCNN) model to identify malignant cells in the cytology images of effusion cytology smears. The training was performed for 15 epochs. The model consisted of 783 layers with 188 convolution-max pool layers in between. In the test set, the DCNN model correctly identified 54 of 56 images of benign samples and 49 out of 56 images of malignant samples. It showed 88% sensitivity, 96% specificity and 96% positive predictive value in the screening of malignant cases in effusion. The area under the receiver operating curve was 0.92. DCNN is a unique technology that can detect malignant cells from cytological images. The model works rapidly and there is no bias in cell selection or feature extraction. The present DCNN model is promising and can have a significant impact on the diagnosis of malignancy in cytology.

Cotton Crop Disease Prediction Using Deep Learning

Cotton was found in 3000 B.C and was first used as a fabric in the Indus Valley. The first mill of cotton in India was established in 1818’s near Kolkata. Cotton is a drought-resistant crop and is best grown in soils with excellent water holding capacity it creates reliable income for the cultivators who cultivate the crop under climate threat. In this study, we have worked on disease detection and crop recommendation system. For disease detection, we have implemented the Deep Convolutional Neural Network(CNN) models i.e., Sequential and VGG-16. In the proposed model we detect whether the cotton crops are infected or not, and if they are further particular disease is identified according to the crop. The highest Accuracy achieved is for VGG16 i.e., 97.53%. The proposed system also recommends medicine with the dosage which can be used to cure the plants. Which can help the farmers in increasing the production and quality of the cotton crop.

Al-Biruni Earth Radius Optimization with Transfer Learning Based Histopathological Image Analysis for Lung and Colon Cancer Detection.

An early diagnosis of lung and colon cancer (LCC) is critical for improved patient outcomes and effective treatment. Histopathological image (HSI) analysis has emerged as a robust tool for cancer diagnosis. HSI analysis for a LCC diagnosis includes the analysis and examination of tissue samples attained from the LCC to recognize lesions or cancerous cells. It has a significant role in the staging and diagnosis of this tumor, which aids in the prognosis and treatment planning, but a manual analysis of the image is subject to human error and is also time-consuming. Therefore, a computer-aided approach is needed for the detection of LCC using HSI. Transfer learning (TL) leverages pretrained deep learning (DL) algorithms that have been trained on a larger dataset for extracting related features from the HIS, which are then used for training a classifier for a tumor diagnosis. This manuscript offers the design of the Al-Biruni Earth Radius Optimization with Transfer Learning-based Histopathological Image Analysis for Lung and Colon Cancer Detection (BERTL-HIALCCD) technique. The purpose of the study is to detect LCC effectually in histopathological images. To execute this, the BERTL-HIALCCD method follows the concepts of computer vision (CV) and transfer learning for accurate LCC detection. When using the BERTL-HIALCCD technique, an improved ShuffleNet model is applied for the feature extraction process, and its hyperparameters are chosen by the BER system. For the effectual recognition of LCC, a deep convolutional recurrent neural network (DCRNN) model is applied. Finally, the coati optimization algorithm (COA) is exploited for the parameter choice of the DCRNN approach. For examining the efficacy of the BERTL-HIALCCD technique, a comprehensive group of experiments was conducted on a large dataset of histopathological images. The experimental outcomes demonstrate that the combination of AER and COA algorithms attain an improved performance in cancer detection over the compared models.

Classification of ECG signals using deep neural networks

The electrocardiogram (ECG) is an essential tool in the field of cardiology, as it enables the electrical activity of the heart to be measured. It involves placing electrodes on the patient's skin, facilitating the measurement and analysis of cardiac rhythms. This non-invasive and painless test provides essential information about the heart's function and helps in diagnosing various cardiac conditions. The classification of ECG signals using deep learning techniques has garnered substantial interest in recent years; ECG classification tasks have exhibited promising outcomes with the application of deep learning models, particularly convolutional neural networks (CNNs). The GoogleNet, AlexNet, and ResNet Deep-CNN models are proposed in this study as reliable methods for accurately diagnosing and classifying cardiac diseases using ECG data. The primary objective of these models is to predict and classify prevalent cardiac ailments, encompassing arrhythmia (ARR), congestive heart failure (CHF), and normal sinus rhythm (NSR). To achieve this classification, 2D Scalogram images obtained through the continuous wavelet transform (CWT) are utilized as input for the models. The study's findings demonstrate that the GoogleNet, AlexNet and Resnet models achieve an impressive accuracy rate of 96%, 95,33% and 92,66%, in accurately predicting and classifying ECG signals associated with these cardiac conditions, respectively. Overall, the integration of deep learning techniques, such as the GoogleNet, AlexNet, and ResNet models, in ECG analysis holds promise for enhancing the accuracy and efficiency of diagnosing and classifying cardiac diseases, potentially leading to improved patient care and outcomes.

A novel Gateaux derivatives with efficient DCNN-Resunet method for segmenting multi-class brain tumor.

In hospitals and pathology, observing the features and locations of brain tumors in Magnetic Resonance Images (MRI) is a crucial task for assisting medical professionals in both treatment and diagnosis. The multi-class information about the brain tumor is often obtained from the patient's MRI dataset. However, this information may vary in different shapes and sizes for various brain tumors, making it difficult to detect their locations in the brain. To resolve these issues, a novel customized Deep Convolution Neural Network (DCNN) based Residual-Unet (ResUnet) model with Transfer Learning (TL) is proposed for predicting the locations of the brain tumor in an MRI dataset. The DCNN model has been used to extract the features from input images and select the Region Of Interest (ROI) by using the TL technique for training it faster. Furthermore, the min-max normalizing approach is used to enhance the color intensity value for particular ROI boundary edges in the brain tumor images. Specifically, the boundary edges of the brain tumors have been detected by utilizing Gateaux Derivatives (GD) method to identify the multi-class brain tumors precisely. The proposed scheme has been validated on two datasets namely the brain tumor, and Figshare MRI datasets for detecting multi-class Brain Tumor Segmentation (BTS).The experimental results have been analyzed by evaluation metrics namely, accuracy (99.78, and 99.03), Jaccard Coefficient (93.04, and 94.95), Dice Factor Coefficient (DFC) (92.37, and 91.94), Mean Absolute Error (MAE) (0.0019, and 0.0013), and Mean Squared Error (MSE) (0.0085, and 0.0012) for proper validation. The proposed system outperforms the state-of-the-art segmentation models on the MRI brain tumor dataset.

Combining 3D skeleton data and deep convolutional neural network for balance assessment during walking.

Introduction: Balance impairment is an important indicator to a variety of diseases. Early detection of balance impairment enables doctors to provide timely treatments to patients, thus reduce their fall risk and prevent related disease progression. Currently, balance abilities are usually assessed by balance scales, which depend heavily on the subjective judgement of assessors. Methods: To address this issue, we specifically designed a method combining 3D skeleton data and deep convolutional neural network (DCNN) for automated balance abilities assessment during walking. A 3D skeleton dataset with three standardized balance ability levels were collected and used to establish the proposed method. To obtain better performance, different skeleton-node selections and different DCNN hyperparameters setting were compared. Leave-one-subject-out-cross-validation was used in training and validation of the networks. Results and Discussion: Results showed that the proposed deep learning method was able to achieve 93.33% accuracy, 94.44% precision and 94.46% F1 score, which outperformed four other commonly used machine learning methods and CNN-based methods. We also found that data from body trunk and lower limbs are the most important while data from upper limbs may reduce model accuracy. To further validate the performance of the proposed method, we migrated and applied a state-of-the-art posture classification method to the walking balance ability assessment task. Results showed that the proposed DCNN model improved the accuracy of walking balance ability assessment. Layer-wise Relevance Propagation (LRP) was used to interpret the output of the proposed DCNN model. Our results suggest that DCNN classifier is a fast and accurate method for balance assessment during walking.

ConvCoroNet: a deep convolutional neural network optimized with iterative thresholding algorithm for Covid-19 detection using chest X-ray images

Covid-19 is a global pandemic. Early and accurate detection of positive cases prevent the further spread of this epidemic and help to treat rapidly the infected patients. During the peak of this epidemic, there was an insufficiency of Covid-19 test kits. In addition, this technique takes a considerable time in the diagnosis. Hence the need to find fast, accurate and low-cost method to replace or supplement RT PCR-based methods. Covid-19 is a respiratory disease, chest X-ray images are often used to diagnose pneumonia. From this perspective, these images can play an important role in the Covid-19 detection. In this article, we propose ConvCoroNet, a deep convolutional neural network model optimized with new method based on iterative thresholding algorithm to detect coronavirus automatically from chest X-ray images. ConvCoroNet is trained on a dataset prepared by collecting chest X-ray images of Covid-19, pneumonia and normal cases from publically datasets. The experimental results of our proposed model show a high accuracy of 99.50%, sensitivity of 98.80% and specificity of 99.85% when detecting Covid-19 from chest X-ray images. ConvCoroNet achieves promising results in the automatic detection of Covid-19 from chest X-ray images. It may be able to help radiologists in the Covid-19 detection by reducing the examination time of X-ray images. Communicated by Ramaswamy H. Sarma

D-t-SNE: Predicting heart disease based on hyper parameter tuned MLP

Heart disease has recently become a major cause of high mortality rates. Concurrently, data mining (DM) has also attracted increasing attention in the healthcare field. Identifying this disease in the starting stage helps to minimize treatment costs, thereby saving people’s lives. Although several classification models have been applied in recent years, they are deficient in their prediction accuracy. Hence, this research intends to apply DM methods for heart disease prediction by concentrating on maximum accuracy. The proposed scheme is evaluated for the performances in terms of various performance metrics using HD datasets (Statlog + Hungary + Cleveland + Switzerland + long beach VA datasets). Deep Convolutional Neural Network (CNN) models have been proposed to extract relevant features owing to their capability for automatic and effective learning. Subsequently, the fusion was performed. Following this, D-t-SNE (Distributed-t-Stochastic Neighborhood Embedding) is introduced to reduce dimensionality reduction to solve over fitting issues and remove redundant data to improve the classifier performance for predicting heart disease. Furthermore, efficient classification is undertaken by the introduced hyper-parameter-tuned MLP (H-MLP), as it has the ability to solve classification issues. Finally, the proposed work was assessed through comparison with traditional techniques with respect to accuracy, precision, sensitivity, Matthew’s correlation coefficient (MCC), F1-score, specificity, and negative predictive value (NPV). The outcomes showed the superior prediction of this system compared to conventional research.