Watershed Segmentation Algorithm Research Articles

Effect of particle size distribution variability on the permeability of hydrate-bearing sediments: A CFD study

Natural gas hydrates (NGH) are considered as a future clean energy in the era of carbon neutrality. The seepage behaviour of hydrate-bearing sediment (HBS) and its controlling factors are significant for developing effective production strategies. In this study, we aim to elucidate how the heterogeneous spatial distribution of clayey-silty sediment particles, particularly the non-uniform particle size distribution (PSD) affects the permeability of HBS. An algorithm was developed to generate the heterogeneous spatial distribution of sediment particles based on the PSD of hydrate-bearing cores extracted from Japan Nankai Trough (D50 = 122 μm). A series simulation of fluid flow in porous media based on CFD was conducted for the HBS with varying hydrate saturations (SH). The watershed segmentation algorithm was employed for the identification of pore structure, including pore space and pore throat with a detailed analysis on the pore structure evolution with SH. Host sediments with higher uniformity result in initial high permeability but the permeability reduction with increasing SH is more significant compared with that of lower uniformity. Pore throat width reduced to less than 5 μm with SH above 50% compared with its original width of ∼30 μm. Tortuosity exhibits a linear increase with SH, while permeability decreases exponentially with increasing tortuosity. We propose an improved permeability model (KCpro) based on the classical Kozeny-Carman model accounting for the change in specific surface area for non-uniform PSD in HBS with good agreement. This study confirmed the applicability of CFD modelling as a promising tool to study the seepage behaviour of HBS complementary to classical permeability experimental tests. Results of this study provide fundamental understanding on how permeability evolves in HBS with significant PSD variations in natural gas hydrate reservoirs in nature.

Pore structure characteristics of coral reef limestone: a combined polarizing microscope and CT scanning study

Coral reef limestone is a special class of geological medium formed through long-term deposition following the death of reef-building coral groups. Because it retains the skeletal structure of marine organisms during its formation, its pore structure is hyper-developed and complex. Deciphering the pore structure of the coral reef limestone is important because it is closely related to its macroscopic physical and mechanical properties. This study conducted a comprehensive analysis of the pore structure features of two types of coral reef limestone collected from the construction site of a nuclear power station located in the South China Sea using a combination of polarizing microscopy and CT scanning technologies. The fractal dimension of the pore structure of the treated reef limestone image was calculated, and the pore structure characteristics were statistically analyzed by considering several parameters including porosity, pore size, pore equivalent radius, shape factor, etc. In addition, the directional feature of the pore structure was explored. The results show that the improved watershed segmentation algorithm can accurately segment the pore structure of reef limestone images; both coral reef limestone specimens are loose of high porosity; the fractal dimension of pore structure lay between 1.58∼1.75, indicative of a high self-similarity; the pore size of the two coral reef limestone specimens is quite different, and the distribution of equivalent pore radius conforms to the normal distribution law; the pore structure of the two samples had obvious directionality, which can be quantified using a directional tensor. This study sheds light on future investigations linking the microscopic structure and macroscopic properties of coral reef limestones.

Examining the Use of the Watershed Algorithm for Segmenting Crown Closure on a Dry Land Forest

This paper uses a watershed algorithm to detect canopy cover in dryland forests. The study at to determine the best parameters of the watershed segmentation algorithm to obtain information on crown closure from filtered and unfiltered high and very high-resolution images. The best performance of each parameter combination of tolerance value (T), mean value (M), and variance value (V), which is written as C:[T]-[M]-[V], is determined based on the level of accuracy. This study uses Pleiades-1B and SPOT-6 images as primary digital data. The results showed that the low-pass filtered Pleiades-1B image showed the best performance with a combination of parameters C6-MF:[10]-[0.7]-[0.3], had an overall accuracy (OA) of 91.0% and an accuracy Kappa (KA) by 83.2%. While the low-pass filtered Spot-6 image shows the combination of parameters C7-MF:[10]-[0.8]-[0.2], which has an accuracy of 90.6% OA and 65.4% KA. This study concludes that the filtered image with a low-pass filter always gives more accurate results than the original data (without filter), both for Pleiades-1B and SPOT-6 images. The very high spatial resolution provides better accuracy than the high spatial resolution

Tree barriers height information extraction under power line based on watershed segmentation and local maximum algorithm

ABSTRACT In view of the problem that the traditional power line inspection cannot accurately obtain the height of the tree barriers under the power line, this study takes a forest area in Guilin City and a power line corridor in Yinchuan City as the research object, and combines watershed segmentation algorithm and local maximum algorithm to study the height of the tree barriers under power line. The results show that the watershed segmentation algorithm and the local maximum algorithm can accurately extract the height of tree barriers in power lines. In the Guilin experiment, there is a good correlation between the extracted height of different trees and the measured height, The correlation coefficients (R2) for osmanthus, eucalyptus, and pine trees are 0.9368, 0.8923, and 0.8227, respectively. In the experiment in Yinchuan, the tree height extracted by unmanned aerial vehicles (UAV) and LiDAR also has a good correlation with the measured height, with correlation coefficients (R2) of 0.926 and 0.952, respectively. These two experiments have verified the feasibility of watershed segmentation algorithm and local maximum algorithm in extracting the height of tree barriers under power lines, which is of great significance for monitoring and warning of tree barriers under power lines.

Estimating the Aboveground Biomass of Robinia pseudoacacia Based on UAV LiDAR Data

Robinia pseudoacacia is widely planted in the Loess Plateau as a major soil and water conservation tree species because of its dense canopy, complex structure, and strong soil and water conservation ability. The precise measurement of small-scale locust forest biomass is crucial to monitoring and evaluating the carbon sequestration functions of soil and water conservation vegetation. This study focuses on an artificial locust forest planted in the early 1990s in Caijiachuan Basin, Jixian County, Shanxi Province. A drone equipped with LiDAR was used to obtain point cloud data and generate a canopy height model. A watershed segmentation algorithm was used to identify tree vertices and extract individual trees. A relationship model between tree height, diameter at breast height, and biomass, combined with sample survey data, was established to explore the spatial distribution of biomass in the artificial locust forest at the level of the entire basin. The results show the following: (1) the structural parameters of locust extracted using UAV point cloud data have a good degree of fit and accuracy, and the recall rate is 72.7%; (2) the average error rate of the extracted maximum tree height value of locust is 7%, that of the minimum tree height value is 14%, and that of the average tree height value is 18%; (3) the average error rate of the extracted maximum diameter at breast height of locust is 15%, that of the minimum diameter at breast height is 37%, and that of the average diameter at breast height is 36%; and (4) the average error rate of the biomass estimation of locust calculated using point cloud data is 16.0%.

Marker-controlled watershed algorithm and fuzzy C-means clustering machine learning: automated segmentation of glioblastoma from MRI images in a case series.

Automated segmentation of glioblastoma multiforme (GBM) from MRI images is crucial for accurate diagnosis and treatment planning. This paper presents a new and innovative approach for automating the segmentation of GBM from MRI images using the marker-controlled watershed segmentation (MCWS) algorithm. The technique involves several image processing techniques, including adaptive thresholding, morphological filtering, gradient magnitude calculation, and regional maxima identification. The MCWS algorithm efficiently segments images based on local intensity structures using the watershed transform, and fuzzy c-means (FCM) clustering improves segmentation accuracy. The presented approach achieved improved segmentation accuracy in detecting and segmenting GBM tumours from axial T2-weighted (T2-w) MRI images, as demonstrated by the mean characteristics performance metrics for GBM segmentation (sensitivity: 0.9905, specificity: 0.9483, accuracy: 0.9508, precision: 0.5481, F_measure: 0.7052, and jaccard: 0.9340). The results of this study underline the importance of reliable and accurate image segmentation for effective diagnosis and treatment planning of GBM tumours. The MCWS technique provides an effective and efficient approach for the segmentation of challenging medical images.

Ore Rock Fragmentation Calculation Based on Multi-Modal Fusion of Point Clouds and Images

The accurate calculation of ore rock fragmentation is important for achieving the autonomous mining operation of mine excavators. However, a single mode cannot accurately calculate the ore rock fragmentation due to the low resolution of the point cloud mode and the lack of spatial position information of the image mode. To solve this problem, we propose an ore rock fragmentation calculation method (ORFCM) based on the multi-modal fusion of point clouds and images. The ORFCM makes full use of the advantages of multi-modal data, including the fine-grained object segmentation of images and spatial location information of point clouds. To solve the problem of image under-segmentation, we propose a multiscale adaptive edge-detection method based on an innovative standard deviation map to enhance the weak edges. Furthermore, an improved marked watershed segmentation algorithm is proposed to solve the problem of low segmentation accuracy caused by excessive noise of the gradient map and weak edges submerged. Experiments demonstrate that ORFCM can accurately calculate ore rock fragmentation in the local excavation area without relying on external markers for pixel calibration. The average error of the equivalent diameter of ore rock blocks is 0.66 cm, the average error of the elliptical long diameter is 1.42 cm, and the average error of the elliptical short diameter is 1.06 cm, which can effectively meet practical engineering needs.

Crown segmentation using Airborne Laser Scanning data in a silvopastoral system with Eucalyptus grandis

Airborne Laser Scanning (ALS) remote sensing holds immense potential for characterizing and measuring forest biomass, providing a valuable inventory tool for intensive silviculture to enhance forest management and planning. Accurate estimation of total height (Ht) and crown area (CA) is crucial to model and design silvopastoral systems. The main objective of this study was to estimate Ht and CA accurately using watershed segmentation algorithm in a triple-row silvopastoral system with Eucalyptus grandis, to compare these variables based on the position of the tree in the rows. Edge trees are the most prevalent in a triple row silvopastoral system, making it important to analyze and understand their structural differences from the trees in the interior. The study was conducted in a silvopastoral system in Reboledo, Florida, Uruguay (34° 1'15.16"S, 55° 39'13.75"W). The silvopastoral system consisted of triple rows of Eucalyptus grandis N-S oriented lined with natural pastures in 14-19 m alleys. ALS data was obtained from an area of 26 ha including two silvopastoral stands planted in 2012 (10 years) and 2019 (4 years). From the canopy height model (CHM) a Watershed segmentation algorithm was conducted to obtain individual tree segmentation, yielding Ht and CA of individual trees in both stands. Individual trees were manually classified by position in the triple row as Middle (M), External West (EW) and External East (EE). Our results show data of 1,627 individual trees in the 2019 stand and 1,405 trees in the 2012 stand, EW trees showed the highest CA in the 2019 stand with a 10% difference with M trees and a 9% with EE trees. The 2019 plot showed no significant difference in Ht. In the 2012 stand, EE trees showed the highest CA with a 22% difference from M trees and an 8% with EW, and EW trees showed a 15% higher CA than M tress in the 2012 stand. The 2012 plot showed a 1% significant average difference between Externals and Middle trees and no significant difference between EE and EW trees. Correlation between field data and ALS data in Ht showed R2=0.82, n=45 and p<0.0001 in 2019 plot, and R2=0.67, n=18, and p<0.0001 in the 2012 plot. Watershed segmentation algorithm in combination with ALS remote sensing demonstrated to be an efficient method to estimate CA and Ht in triple-row silvopastoral systems.

Integrating watershed segmentation algorithm and supervised Bayesian classification for the assessment of petrophysical parameters, pore properties, and lithofacies: a case study from Abadan Plain, SW Iran

Integrating watershed segmentation algorithm and supervised Bayesian classification for the assessment of petrophysical parameters, pore properties, and lithofacies: a case study from Abadan Plain, SW Iran

Automated segmentation of meningioma from contrast-enhanced T1-weighted MRI images in a case series using a marker-controlled watershed segmentation and fuzzy C-means clustering machine learning algorithm

Introduction and importanceAccurate segmentation of meningiomas from contrast-enhanced T1-weighted (CE T1-w) magnetic resonance imaging (MRI) is crucial for diagnosis and treatment planning. Manual segmentation is time-consuming and prone to variability. To evaluate an automated segmentation approach for meningiomas using marker-controlled watershed segmentation (MCWS) and fuzzy c-means (FCM) algorithms. Case presentation and methodsCE T1-w MRI of 3 female patients (aged 59, 44, 67 years) with right frontal meningiomas were analyzed. Images were converted to grayscale and preprocessed with Otsu's thresholding and FCM clustering. MCWS segmentation was performed. Segmentation accuracy was assessed by comparing automated segmentations to manual delineations. Clinical discussionThe approach successfully segmented meningiomas in all cases. Mean sensitivity was 0.8822, indicating accurate identification of tumors. Mean Dice similarity coefficient between Otsu's and FCM1 was 0.6599, suggesting good overlap between segmentation methods. ConclusionThe MCWS and FCM approach enables accurate automated segmentation of meningiomas from CE T1-w MRI. With further validation on larger datasets, this could provide an efficient tool to assist in delineating meningioma boundaries for clinical management.

Remote Sensing Parameter Extraction of Artificial Young Forests under the Interference of Undergrowth

Due to the lower canopy height at the maximum crown width at the bottom of young Picea crassifolia trees, they are mixed with undergrowth. This makes it challenging to accurately determine crown size using CHM data or point cloud data. UAV imagery, on the other hand, incorporates rich color information and, after processing, can effectively distinguish between spruce trees and ground vegetation. In this study, the experimental site was an artificial young forest of Picea crassifolia in Shangshan Village, Qinghai Province, China. UAV images were used to obtain normalized saturation data for the sample plots. A marker-controlled watershed segmentation algorithm was employed to extract tree parameters, and the results were compared with those obtained via point cloud clustering segmentation and the marker-controlled watershed segmentation algorithm based on Canopy Height Model (CHM) images. The research results showed that the single tree recognition capabilities of the three types of data were similar, with F-measures of 0.96, 0.95, and 0.987 for the CHM image, UAV imagery, and point cloud data, respectively. The mean square errors of crown width information extracted from the UAV imagery using the marker-controlled watershed segmentation algorithm were 0.043, 0.125, and 0.046 for the three sample plots, which were better than the values of 0.103, 0.182, and 0.074 obtained from CHM data, as well as the values of 0.36, 0.461, and 0.4 obtained from the point cloud data. The point cloud data exhibited better fitting results for tree height extraction compared to the CHM images. This result indicates that UAV-acquired optical imagery has applicability in extracting individual tree feature parameters and can compensate for the deficiencies of CHM and point cloud data.

Medical image watermarking based on novel encoding for EHR and fusion based morphological watershed segmentation algorithm for medical images

Medical image watermarking based on novel encoding for EHR and fusion based morphological watershed segmentation algorithm for medical images

MDEEPFIC: Food item classification with calorie calculation using modified dragonfly deep learning network

Foods are very essential for living beings for providing energy, development and preserve their existence. It plays a vital role in promoting health and preventing illness. Nowadays, many people are suffered from obesity, they tend to maintain their body weight by consuming a sufficient number of calories in their routine life. In this research, a novel Modified Deep Learning-based Food Item Classification (MDEEPFIC) approach has been proposed to categorize the different food items from the dataset with their calorie values. Initially, the images are processed using the sigmoid stretching method to enhance the image quality and remove the noises. Consequently, the pre-processed images are segmented using Improved Watershed Segmentation (IWS2) algorithm. Recurrent Neural Network (RNN) is used to extract features like shape, size, textures, and color. The extracted features are then normalized using the modified dragonfly technique for same food calorie calculation. Bidirectional Long Short-Term Memory (Bi-LSTM) is utilized to classify food products based on these pertinent aspects. Finally, using food area volume and calorie and nutrition measures based on mass value, the calorie value of the categorized food item is calculated. The efficiency of the proposed method was calculated in terms of specificity, precision, accuracy, and recall F-measure. The proposed method improves the overall accuracy of 4.99%, 8.72%, and 10.4% better than existing Deep Convolution Neural Network (DCNN), Faster Recurrent convolution neural network (FRCNN), Local Variation Segmentation based Support Vector Machine (LSV-SVM) method respectively.

Detection of early bruises on apples using hyperspectral reflectance imaging coupled with optimal wavelengths selection and improved watershed segmentation algorithm.

Bruises caused by mechanical collision during the harvesting and storage and transportation period are difficult to detect using traditional machine vision technologies because there is no obvious difference in appearance between bruised and sound tissues. As a result of its fast and non-destructive characteristics, hyperspectral imaging technology is a potential tool for non-destructive detection of fruit surface defects. In the present study, visible near infrared hyperspectral reflectance images of healthy apples and bruised apples at 6, 12 and 24 h were obtained. To reduce hyperspectral data dimension, optimal wavelength selection algorithms including principal component analysis (PCA) and band ratio methods were utilized to select the effective wavelengths and enhance the contrast between bruised and sound tissues. Then pseudo-color image transformation technology combining with improved watershed segmentation algorithm (IWSA) were employed to recognize the bruise spots. The result obtained showed that band ratio images obtained better detection performance than that of PCA. The G component derived from pseudo-color image of followed by IWSA obtained the best segmentation performance for bruise spots. Finally, a multispectral imaging system for the detection of bruised apple was developed to verify the effectiveness of the proposed two-band ratio algorithm, obtaining recognition rates of 93.3%, 92.2% and 92.5% for healthy, bruised and overall apples, respectively. The bruise detection algorithm proposed in the present study has potential to detect bruised apple in online practical applications and hyperspectral reflectance imaging offers a useful reference for the detection of surficial defects of fruit. © 2023 Society of Chemical Industry.

AN AI SEGMENTER ON MEDICAL IMAGING FOR GEOMATICS APPLICATIONS CONSISTING OF A TWO-STATE PIPELINE, SNNS NETWORK AND WATERSHED ALGORITHM

Abstract. As is well known, image segmentation is widely used in the fields of echocardiography and diagnostic and interventional radiology. The delineation of structural components of various organs from 2D images is a technique used in the medical field in order to identify intervention targets with increasing precision and accuracy. In recent decades, the automation of this task has been the subject of intensive research. In particular, to improve the segmentation of such images, investigations have focused on the use of neural networks, and in particular convolutional neural networks (CNNs). However, most existing CNN-based methods can produce unsatisfactory segmentation masks without precise object boundaries (Wang, Chen, Ji, Fan & Ye Li, 2022); this is mainly due to the shadows and high noise in these images. To address the problem of automated image segmentation, this work proposes a pipeline technique with two stages (applied primarily to the echocardiographic domain): the first consisting of a Self-normalising Neural Networks (SNNs) performs image denoising, while the second applies a Watershed segmentation algorithm on the cleaned image. The latter is a technique successfully applied in geomatics and land surveying. The proposed methodology may be of interest both in the medical field and in the field of Geomatics where segmentation and classification operations are required in different application areas.

The effect of coated diamond-like carbon thin films on polymer tooth based denture: Micro-morphology and fractal feature studies

To consider improving the micromorphology study of dentures combined with biocompatibility in recent years, we study hybrid organic–inorganic materials and diamond-like carbon coated on tooth-based dentures by the sol–gel and the RF sputtering method, respectively. Afterward, the effect of such material coating on the surface roughness and surface topography of dentures was evaluated. Aimed at this, 3D atomic force microscope images were analyzed via MountainsMap® Premium software, which divided the surface into peaks and pits through the watershed segmentation algorithm. This method made motif analysis possible by detecting the surface dimensions, curvature, volume, perimeters, shape, structure, etc., and the obtained statistics indicate interesting results for the regular and irregular topography and the surface of the prepared samples. In addition, the energy-dispersive x-ray spectrum illustrates the existence of N, C, O, Na, and Si in all samples, with the maximum value of C in the diamond-like carbon coated sample, which is because of the highest value of carbon in diamond-like carbon. Scanning electron microscope images also present a series of grooves on the surface with small randomly oriented cracks. The linear interpolation type shows that the surface of diamond-like carbon coated on the hybrid-control sample has the most irregular topography while the most regular topography was found in the hybrid coated control sample.

MRI Image-Based Automatic Segmentation and Classification of Brain Tumor and Swelling Using Novel Methodologies

In the medical image analysis field, brain tumors (BTs) classification is a complicated process. For effortlessly detecting the tumor devoid of any surgical interference, the radiologists are aided with automated along with computerized technology. Currently, in the field of medical image processing along with analysis, admirable progress has been made by deep learning (DL) methodologies. In medical fields, for resolving several issues, huge attention was paid to DL techniques. For automation of several performed by radiologists like (1) lesion detection, (2) segmentation, (3) classification, (4) monitoring, along with (5) also prediction of treatment response that is not achievable without software, DL might be wielded. Nevertheless, classifying BTs by utilizing magnetic resonance imaging (MRI) has various complications like the difficulty of brain structure along with the intertwining of tissues in it; additionally, the brain’s higher density nature also makes the BT Classification (BTC) process quite complex. Therefore, by utilizing novel systems, MRI-centric Automatic segmentation together with classifications of BT and swelling have been proposed to overcome the aforementioned issues. The proposed methodology underwent various operations to detect BTs effectively. Initially, by utilizing the Range-centric Otsu’s Thresholding (ROT) algorithm, the skull stripping (SS) is conducted. After that, by performing contrast enhancement (CE) along with noise removal, the skull-stripped images are pre-processed. Next, by employing the Rectilinear Watershed Segmentation (RWS) algorithm, the tumor or swelling areas are segmented. Afterward, to obtain the precise tumor or swelling region, the morphological operations are executed on the segmented areas; subsequently, the desired along with relevant features are extracted. Lastly, the features being extracted are inputted to the classifier termed Uniform Convolution neural network (UCNN). The tumor tissues along with the swelling tissues are classified precisely in the classification phase. Here, the openly accessible BT Image Segmentation Benchmark (BRATS) datasets are utilized. Then, the outcomes obtained are analogized with prevailing methodologies. The experiential outcomes displayed that the BTC is performed by the proposed model with a higher accuracy rate; thus, outshined the other prevailing models.

Automatic Segmentation and Classification for Antinuclear Antibody Images Based on Deep Learning.

Antinuclear antibodies (ANAs) testing is the main serological diagnosis screening test for autoimmune diseases. ANAs testing is conducted principally by the indirect immunofluorescence (IIF) on human epithelial cell-substrate (HEp-2) protocol. However, due to its high variability and human subjectivity, there is an insistent need to develop an efficient method for automatic image segmentation and classification. This article develops an automatic segmentation and classification framework based on artificial intelligence (AI) on the ANA images. The Otsu thresholding method and watershed segmentation algorithm are adopted to segment IIF images of cells. Moreover, multiple texture features such as scale-invariant feature transform (SIFT), local binary pattern (LBP), cooccurrence among adjacent LBPs (CoALBP), and rotation invariant cooccurrence among adjacent LBPs (RIC-LBP) are utilized. Firstly, this article adopts traditional machine learning methods such as support vector machine (SVM), k-nearest neighbor algorithm (KNN), and random forest (RF) and then uses ensemble classifier (ECLF) combined with soft voting rules to merge these machine learning methods for classification. The deep learning method InceptionResNetV2 is also utilized to train on the classification of cell images. Eventually, the best accuracy of 0.9269 on the Changsha dataset and 0.9635 on the ICPR 2016 dataset for the traditional methods is obtained by a combination of SIFT and RIC-LBP with the ECLF classifier, and the best accuracy obtained by the InceptionResNetV2 is 0.9465 and 0.9836 separately, which outperforms other schemes.

High-precision automatic identification method for dicentric chromosome images using two-stage convolutional neural network

Dicentric chromosome analysis is the gold standard for biological dose assessment. To enhance the efficiency of biological dose assessment in large-scale radiation catastrophes, automatic identification of dicentric chromosome images is a promising and objective method. In this paper, an automatic identification method for dicentric chromosome images using two-stage convolutional neural network is proposed based on Giemsa-stained automatic microscopic imaging. To automatically segment the adhesive chromosome masses, a k-means based adaptive image segmentation and watershed segmentation algorithm is applied. The first-stage CNN is used to identify the dicentric chromosome images from all the images and the second-stage CNN works to specifically identify the dicentric chromosome images. This two-stage CNN identification method can effectively detects chromosome images with concealed centromeres, poorly expanded and long-armed entangled chromosomes, and tricentric chromosomes. The novel two-stage CNN method has a chromosome identification accuracy of 99.4%, a sensitivity of 85.8% sensitivity, and a specificity of 99.6%, effectively reducing the false positive rate of dicentric chromosome. The analysis speed of this automatic identification method can be 20 times quicker than manual detection, providing a valuable reference for other image identification situations with small target rates.

Automated Pneumonia Based Lung Diseases Classification with Robust Technique Based on a Customized Deep Learning Approach.

Many people have been affected by infectious lung diseases (ILD). With the outbreak of the COVID-19 disease in the last few years, many people have waited for weeks to recover in the intensive care wards of hospitals. Therefore, early diagnosis of ILD is of great importance to reduce the occupancy rates of health institutions and the treatment time of patients. Many artificial intelligence-based studies have been carried out in detecting and classifying diseases from medical images using imaging applications. The most important goal of these studies was to increase classification performance and model reliability. In this approach, a powerful algorithm based on a new customized deep learning model (ACL model), which trained synchronously with the attention and LSTM model with CNN models, was proposed to classify healthy, COVID-19 and Pneumonia. The important stains and traces in the chest X-ray (CX-R) image were emphasized with the marker-controlled watershed (MCW) segmentation algorithm. The ACL model was trained for different training-test ratios (90-10%, 80-20%, and 70-30%). For 90-10%, 80-20%, and 70-30% training-test ratios, accuracy scores were 100%, 96%, and 96%, respectively. The best performance results were obtained compared to the existing methods. In addition, the contribution of the strategies utilized in the proposed model to classification performance was analyzed in detail. Deep learning-based applications can be used as a useful decision support tool for physicians in the early diagnosis of ILD diseases. However, for the reliability of these applications, it is necessary to undertake verification with many datasets.