Noise Removal Process Research Articles

Optimal segmentation and fusion of multi-modal brain images using clustering based deep learning algorithm

In the last several years, the world of medical technology has seen a boom in multimodal picture fusion. Information is constrained since a single medical instrument can only acquire single modal pictures. Doctors often need a large number of multimodal pictures to get the complete information necessary for disease diagnosis. The burden associated with illness diagnosis will significantly rise when multimodal pictures are employed directly, and errors and interference are likely to occur. Fusion algorithms, which have been extensively employed in the medical industry, may very effectively combine a lot of information in multimodal pictures. However, the existing method has an issue with the earlier stages of brain tumor prediction in white images and inaccuracy image results. To overcome the above-mentioned problems, in this work, Adaptive Firefly Optimization based Convolutional Neural Network (AFFOCNN) and Modified Fully Connected Layer (MFCL) scheme is proposed. This work contains main steps such as noise removal, segmentation, feature extraction, image fusion, and image classification process. Initially, noise removal is done for improving the image quality by removing the noise. Then the modality MRI images are segmented and it is used for subdividing an image into its constituent regions or object. It segments the image into black and white images. After that, feature extraction is applied through the AFFOCNN algorithm which extracts the more informative image features. Image fusion of multi-modal images derived the lower-level, middle-level, and higher-level image contents. It can be viewed in multiple directions and fused in all directions. Finally, image classification is performed by using a Modified Fully Connected Layer (MFCL) which improves the training and testing features efficiently. It was determined from the results that the suggested combination of AFFOCN and MFCL algorithm performs improved than the current algorithms with the increased accuracy, precision, recall, and mean square error (MSE), as well as execution time with the values of 99.00%, 98.00%, 96.00%, 12.00% and 2.40 seconds respectively.

Automated Colonic Polyp Detection and Classification Enabled Northern Goshawk Optimization with Deep Learning

The major mortality factor relevant to the intestinal tract is the growth of tumorous cells (polyps) in various parts. More specifically, colonic polyps have a high rate and are recognized as a precursor of colon cancer growth. Endoscopy is the conventional technique for detecting colon polyps, and considerable research has proved that automated diagnosis of image regions that might have polyps within the colon might be used to help experts for decreasing the polyp miss rate. The automated diagnosis of polyps in a computer-aided diagnosis (CAD) method is implemented using statistical analysis. Nowadays, Deep Learning, particularly through Convolution Neural networks (CNN), is broadly employed to allow the extraction of representative features. This manuscript devises a new Northern Goshawk Optimization with Transfer Learning Model for Colonic Polyp Detection and Classification (NGOTL-CPDC) model. The NGOTL-CPDC technique aims to investigate endoscopic images for automated colonic polyp detection. To accomplish this, the NGOTL-CPDC technique comprises of adaptive bilateral filtering (ABF) technique as a noise removal process and image pre-processing step. Besides, the NGOTL-CPDC model applies the Faster SqueezeNet model for feature extraction purposes in which the hyperparameter tuning process is performed using the NGO optimizer. Finally, the fuzzy Hopfield neural network (FHNN) method can be employed for colonic poly detection and classification. A widespread simulation analysis is carried out to ensure the improved outcomes of the NGOTL-CPDC model. The comparison study demonstrates the enhancements of the NGOTL-CPDC model on the colonic polyp classification process on medical test images.

Artifact removal from sEMG signals recorded during fully unsupervised daily activities.

In this study, we propose a method for removing artifacts from superficial electromyography (sEMG) data, which have been widely proposed for health monitoring because they encompass the basic neuromuscular processes underlying human motion. Our method is based on a spectral source decomposition from single-channel data using a non-negative matrix factorization. The algorithm is validated with two data sets: the first contained muscle activity coupled to artificially generated noises and the second comprised signals recorded under fully unsupervised conditions. Algorithm performance was further assessed by comparison with other state-of-the-art approaches for noise removal using a single channel. The comparison of methods shows that the proposed algorithm achieves the highest performance on the noise-removal process in terms of signal-to-noise ratio reconstruction, root means square error, and correlation coefficient with the original muscle activity. Moreover, the spectral distribution of the extracted sources shows high correlation with the noise sources traditionally associated to sEMG recordings. This research shows the ability of spectral source separation to detect and remove noise sources coupled to sEMG signals recorded during unsupervised daily activities which opens the door to the implementation of sEMG recording during daily activities for motor and health monitoring.

Investigating Deep learning models for NFT classification : A Review

Present research work is focused on NFT image classification using deep learning techniques. The classification task is extended by incorporating noise removal techniques in addition to image compression before employing the CNN model. After collecting and splitting the dataset, the images undergo both compression and noise removal processes to enhance their visual quality and reduce unwanted artifacts. The preprocessed images are then fed into a CNN model for training and evaluation. This approach aims to improve the model's ability to discern features in images by minimizing the impact of noise. The evaluation phase involves assessing the model's performance on the test set to determine its effectiveness in classifying NFT images after both compression and noise removal.

Optimal Fuzzy Wavelet Neural Network Based Road Damage Detection

Floods are one of the most severe and most frequent natural calamities. It causes enormous economic damage and even leads to higher mortality rates. Studies on damage detection of roads using artificial intelligence (AI) methods gained more attention currently, especially in the development of smart cities. Therefore, this study designs an optimal Fuzzy Wavelet Neural Network based Road Damage Detection (OFWNN-RDD) technique for Flooding Management. The OFWNN-RDD technique aims to exploit the remote sensing images to classify different kinds of roads. For noise removal process, the OFWNN-RDD technique utilizes Gabor filtering (GF) technique. In addition, the OFWNN-RDD technique uses the DenseNet121 model for feature vector generation with modified barnacles mating optimization (MBMO) based hyperparameter optimizer. Finally, FWNN image classification approach is used for road damage detection. The simulation values exhibit the supremacy of the OFWNN-RDD technique over other models with improved road damage detection accuracy of 98.56%.

Squirrel Search Optimization with Deep Convolutional Neural Network for Human Pose Estimation

Human pose estimation (HPE) is a procedure for determining the structure of the body pose and it is considered a challenging issue in the computer vision (CV) communities. HPE finds its applications in several fields namely activity recognition and human-computer interface. Despite the benefits of HPE, it is still a challenging process due to the variations in visual appearances, lighting, occlusions, dimensionality, etc. To resolve these issues, this paper presents a squirrel search optimization with a deep convolutional neural network for HPE (SSDCNN-HPE) technique. The major intention of the SSDCNN-HPE technique is to identify the human pose accurately and efficiently. Primarily, the video frame conversion process is performed and pre-processing takes place via bilateral filtering-based noise removal process. Then, the EfficientNet model is applied to identify the body points of a person with no problem constraints. Besides, the hyperparameter tuning of the EfficientNet model takes place by the use of the squirrel search algorithm (SSA). In the final stage, the multiclass support vector machine (M-SVM) technique was utilized for the identification and classification of human poses. The design of bilateral filtering followed by SSA based EfficientNet model for HPE depicts the novelty of the work. To demonstrate the enhanced outcomes of the SSDCNN-HPE approach, a series of simulations are executed. The experimental results reported the betterment of the SSDCNN-HPE system over the recent existing techniques in terms of different measures.

Modified Barnacles Mating Optimization with Deep Learning Based Weed Detection Model for Smart Agriculture

Weed control is a significant means to enhance crop production. Weeds are accountable for 45% of the agriculture sector’s crop losses, which primarily occur because of competition with crops. Accurate and rapid weed detection in agricultural fields was a difficult task because of the presence of a wide range of weed species at various densities and growth phases. Presently, several smart agriculture tasks, such as weed detection, plant disease detection, species identification, water and soil conservation, and crop yield prediction, can be realized by using technology. In this article, we propose a Modified Barnacles Mating Optimization with Deep Learning based weed detection (MBMODL-WD) technique. The MBMODL-WD technique aims to automatically identify the weeds in the agricultural field. Primarily, the presented MBMODL-WD technique uses the Gabor filtering (GF) technique for the noise removal process. For automated weed detection, the presented MBMODL-WD technique uses the DenseNet-121 model as feature extraction with the MBMO algorithm as hyperparameter optimization. The design of the MBMO algorithm involves the integration of self-population-based initialization with the standard BMO algorithm. At last, the Elman Neural Network (ENN) method was applied for the weed classification process. To demonstrate the enhanced performance of the MBMODL-WD approach, a series of simulation analyses were performed. A comprehensive set of simulations highlighted the enhanced performance of the presented MBMODL-WD methodology over other DL models with a maximum accuracy of 98.99%.

A robust multiple heartbeats classification with weight-based loss based on convolutional neural network and bidirectional long short-term memory.

Background: Analysis of electrocardiogram (ECG) provides a straightforward and non-invasive approach for cardiologists to diagnose and classify the nature and severity of variant cardiac diseases including cardiac arrhythmia. However, the interpretation and analysis of ECG are highly working-load demanding, and the subjective may lead to false diagnoses and heartbeats classification. In recent years, many deep learning works showed an excellent role in accurate heartbeats classification. However, the imbalance of heartbeat classes is universal in most of the available ECG databases since abnormal heartbeats are always relatively rare in real life scenarios. In addition, many existing approaches achieved prominent results by removing noise and extracting features in data preprocessing, which relies heavily on powerful computers. It is a pressing need to develop efficient and automatic light weighted algorithms for accurate heartbeats classification that can be used in portable ECG sensors. Objective: This study aims at developing a robust and efficient deep learning method, which can be embedded into wearable or portable ECG monitors for classifying heartbeats. Methods: We proposed a novel and light weighted deep learning architecture with weight-based loss based on a convolutional neural network (CNN) and bidirectional long short-term memory (Bi-LSTM) that can automatically identify five types of ECG heartbeats according to the AAMI EC57 standard. It was also true that the raw ECG signals were simply segmented without noise removal and other feature extraction processing. Moreover, to tackle the challenge of classification bias due to imbalanced ECG datasets for different types of arrhythmias, we introduced a weight-based loss function to reduce the influence of over-weighted categories in the ECG dataset. For avoiding the influence of the division of validation dataset, k-fold method was adopted to improve the reliability of the model. Results: The proposed algorithm is trained and tested on MIT-BIH Arrhythmia Database, and achieves an average of 99.33% accuracy, 93.67% sensitivity, 99.18% specificity, 89.85% positive prediction, and 91.65% F1 score.

Manta Ray Foraging Optimization Transfer Learning-Based Gastric Cancer Diagnosis and Classification on Endoscopic Images.

Gastric cancer (GC) diagnoses using endoscopic images have gained significant attention in the healthcare sector. The recent advancements of computer vision (CV) and deep learning (DL) technologies pave the way for the design of automated GC diagnosis models. Therefore, this study develops a new Manta Ray Foraging Optimization Transfer Learning technique that is based on Gastric Cancer Diagnosis and Classification (MRFOTL-GCDC) using endoscopic images. For enhancing the quality of the endoscopic images, the presented MRFOTL-GCDC technique executes the Wiener filter (WF) to perform a noise removal process. In the presented MRFOTL-GCDC technique, MRFO with SqueezeNet model is used to derive the feature vectors. Since the trial-and-error hyperparameter tuning is a tedious process, the MRFO algorithm-based hyperparameter tuning results in enhanced classification results. Finally, the Elman Neural Network (ENN) model is utilized for the GC classification. To depict the enhanced performance of the presented MRFOTL-GCDC technique, a widespread simulation analysis is executed. The comparison study reported the improvement of the MRFOTL-GCDC technique for endoscopic image classification purposes with an improved accuracy of 99.25%.

An improved Gaussian Mixture Model with post-processing for multiple object detection in surveillance video analytics

Gaussian Mixture Model (GMM) is an effective method for extracting foreground objects from video sequences. However, GMM fails to detect the object in challenging scenarios like the presence of shadow, occlusion, complex backgrounds, etc. To handle these challenges, intrinsic and extrinsic enhancement is required in traditional GMM. This paper presents a novel framework that combines improved GMM with postprocessing for multiple object detection. In the proposed system, GMM with parameter initialization is considered an intrinsic improvement. Video preprocessing and postprocessing are considered extrinsic improvements. Integration of morphological operation with GMM helps for better segmentation than traditional GMM, and it also helps to increase detection performance by reducing false positives. Video preprocessing is the process of noise removal that prepares input video ready for further processing. In the final step gradient of morphological operations is used for postprocessing. The proposed approach was tested on challenging surveillance video sequences from benchmark datasets such as PETS 2009 and CD 2014(Change Detection). The experimental results are compared using ground truth and performance evaluation metrics. The results show that the proposed approach performs better than GMM, and the method can detect the object effectively even in illumination variation and partial occlusion.

Segmentation of human spermatozoa using improved Havrda-Chavrat entropy-based thresholding method

The assisted method of fertilization has required an identification of sperm cells with normal morphological structure. The abnormal sperm cells cannot provide successful result in artificial fertilization. Nowadays the assessment of morphology of sperm cells is subjective and error prone hence creating automatic evaluation method for morphology assessment, it will improve the success ratio in infertility treatment. The first step in our proposed system is pre-processing where noise removal process is applied on microscopic medical images. In second step, adaptive alpha valued Havrda-Chavrat entropy-based threshold technique is proposed where the maximum probability distribution of foreground pixels or background pixels is assigned to alpha value. Further, existing state-of-art threshold-based segmentation methods are implemented and obtained results on the input images. These segmentation results are compared with the proposed method in terms of supervised and unsupervised evaluation metrics, in which our proposed thresholding method has given optimum threshold value for the segmentation of spermatozoa cells. The outcome of the segmented images and their metric values are indicating better segmentation by our proposed method. Furthermore, this proposed method can be implemented in the mobile applications for diagnosis with artificial intelligence techniques.

Speckle Noise Removal by SORAMA Segmentation in Digital Image Processing to Facilitate Precise Robotic Surgery

Kidney stones are renal calculi that are formed due to the collection of calcium and uric acid. The major symptom for the existence of these renal calculi is severe pain, especially when it travels down the urethras To detect these renal calculi, ultrasound images are preferable. But these images have speckle noise which makes the detection of stone challenge. To obtain better results, Semantic Object Region and Morphological Analysis (SORAMA) found to be productive. First scanned image undergoes noise removal process Later the image is enhanced. Detection of Region of interest (ROI) in the image is done. Later it undergoes Dilation and Erosion were a part of Morphological analysis which produces a smoothening effect on the image. From the smoothened image, the stone is detected. If the stone is not detected then it again undergoes noise removal technique and the whole process is repeated until the smoothened image with the stone is detected. This novel research paper will be a boon to medical patients suffering from this disease to be detected and diagnose at a very early stage.

A type-2 fuzzy system-based approach for image data fusion to create building information models

Building Information Modelling (BIM) is a standard digital process that fuses buildings information from different sources into a 3D model during their lifecycle. For new construction sites using BIM, it is possible to monitor the cost, schedule, and changes throughout the lifecycle; however, existing buildings do not have a BIM model. Manually creating the BIM models for existing buildings is a high-cost task, both in time and money, hence there is a need for extracting information from available paper-based documentation and fuse it into a BIM model. The struggle of facility management and utility companies to fully adopt a BIM process (due to their high volumes of paper-based documentation of existing buildings) has led to the research on creating these 3D BIM models from 2D floor plan images.This paper presents a novel processing pipeline to extract 2D digital information from floorplans, fusing it into a 3D BIM model. The work focuses on fusing the available information to create the structure of the building in BIM format, which is considered the essential step before looking on working with other sources of data. In this process, we introduce a type-2 fuzzy logic based Explainable Artificial Intelligence (XAI) approach for the semantic segmentation step. The approach consists of using the outputs of type-2 fuzzy logic systems to classify a pixel as wall or background, by using information around and from the pixel of interest as the inputs to the system. After the semantic segmentation step, the output of the type-2 fuzzy logic goes through a noise removal process and finally a transformation from 2D to 3D by assigning the corresponding BIM tag to each identified element. The proposed type-2 fuzzy logic semantic segmentation approach produced comparable results (97.3% mean Intersection over Union (IoU) performance metric value) to the opaque box model approach based on Convolutional Neural Network (CNN) (99.3% mean IoU performance metric value). However, the type-2 fuzzy XAI system benefits from being an augmentable and interpretable model, which means that human users can understand the decision process and modify the model using their expert knowledge.

Classification of Glaucoma Based on Elephant-Herding Optimization Algorithm and Deep Belief Network

This study proposes a novel glaucoma identification system from fundus images through the deep belief network (DBN) optimized by the elephant-herding optimization (EHO) algorithm. Initially, the input image undergoes the preprocessing steps of noise removal and enhancement processes, followed by optical disc (OD) and optical cup (OC) segmentation and extraction of structural, intensity, and textural features. Most discriminative features are then selected using the ReliefF algorithm and passed to the DBN for classification into glaucomatous or normal. To enhance the classification rate of the DBN, the DBN parameters are fine-tuned by the EHO algorithm. The model has experimented on public and private datasets with 7280 images, which attained a maximum classification rate of 99.4%, 100% specificity, and 99.89% sensitivity. The 10-fold cross validation reduced the misclassification and attained 98.5% accuracy. Investigations proved the efficacy of the proposed method in avoiding bias, dataset variability, and reducing false positives compared to similar works of glaucoma classification. The proposed system can be tested on diverse datasets, aiding in the improved glaucoma diagnosis.

Morphological Structural Element Optimization for Automatic Line Characterization of Ink Drawings

As a traditional Chinese art, the tools of Chinese ink painting are mainly brushes, ink, and rice paper and ink stone. As an important form of ink painting, ink landscape painting consists of ink lines that first outline the overall trend of the mountain and then complete the basic process of painting with rich chapping techniques. The article proposes a process to draw water animation based on Chinese painting style; this method first perceives the position of water form in Chinese painting and enhances it by morphological structural elements used to automatically draw the line feature analysis of ink painting; next, the image rendering technique is used to render the water form, firstly using BF for noise removal processing and then gradienting by Canny edge detection operator to get the gradient amplitude. Next, morphological optimization is applied, and finally the image is visualized. In the process of morphological optimization, the optimal parameters of the structural elements are found through the threshold segmentation number, and the rendering algorithm for ink-based landscape painting is explored in depth, starting from the ink lines. The results reveal that the method is more accurate than typical marker-watershed algorithm segmentation, and that changing different structural elements and their parameters has a bigger impact on the ink painting drawing effect.

An Intelligent Deep Learning Enabled Marine Fish Species Detection and Classification Model

In recent times, marine fish species recognition becomes an important research area to protect the ocean environment. It is a tough and time-consuming operation to manually detect marine fish species on the ocean floor. Depending on the situation, extensive sample efforts may be required. These efforts might be harmful to the marine ecosystem. Automated classification methods are capable of properly classifying these fish on a consistent basis. An increasing number of people are becoming interested in utilizing electronic monitoring and reporting with artificial intelligence for the aim of fish identification and enhancing present techniques. It is becoming more usual to use video and pictures of fish (either underwater or on ships) in fishing operations. These techniques are operational, transportable, and non-invasive, and they provide high-quality pictures at a lower cost than traditional approaches. Automated image processing techniques such as Deep Learning (DL) and Machine Learning (ML) are now available, and they may be customized to perform efficient fish species identification and segmentation. In this aspect, this paper presents an Intelligent DL based Marine Fish Species Classification (IDL-MFSC) technique. The proposed IDL-MFSC technique involves three major processes such as pre-processing, fish detection and fish classification. Primarily, Weiner filtering-based noise removal process takes place as a pre-processing step. In addition, Mask R-CNN (Mask Region Based Convolutional Neural Networks) with Residual Network as a backbone network is used for fish detection. Moreover, Optimal Deep Kernel Extreme Learning Machine (ODKELM) based classification method is employed for determining the class labels of the marine fish species in which the parameter tuning of the DKELM model takes place using Water Wave Optimization (WWO) technique. The performance of the proposed method is tested using an openly accessible Fish4Knowledge dataset. The experimental result highlights the supremacy of the IDL-MFSC technique over the recent techniques with respect to various measures.

Optimal Deep Convolution Neural Network for Cervical Cancer Diagnosis Model

Biomedical imaging is an effective way of examining the internal organ of the human body and its diseases. An important kind of biomedical image is Pap smear image that is widely employed for cervical cancer diagnosis. Cervical cancer is a vital reason for increased women’s mortality rate. Proper screening of pap smear images is essential to assist the earlier identification and diagnostic process of cervical cancer. Computer-aided systems for cancerous cell detection need to be developed using deep learning (DL) approaches. This study introduces an intelligent deep convolutional neural network for cervical cancer detection and classification (IDCNN-CDC) model using biomedical pap smear images. The proposed IDCNN-CDC model involves four major processes such as preprocessing, segmentation, feature extraction, and classification. Initially, the Gaussian filter (GF) technique is applied to enhance data through noise removal process in the Pap smear image. The Tsallis entropy technique with the dragonfly optimization (TE-DFO) algorithm determines the segmentation of an image to identify the diseased portions properly. The cell images are fed into the DL based SqueezeNet model to extract deep-learned features. Finally, the extracted features from SqueezeNet are applied to the weighted extreme learning machine (ELM) classification model to detect and classify the cervix cells. For experimental validation, the Herlev database is employed. The database was developed at Herlev University Hospital (Denmark). The experimental outcomes make sure that higher performance of the proposed technique interms of sensitivity, specificity, accuracy, and F-Score.

A fuzzy edge detector driven telegraph total variation model for image despeckling

<p style='text-indent:20px;'>Speckle noise suppression is a challenging and crucial pre-processing stage for higher-level image analysis. In this work, a new attempt has been made using telegraph total variation equation and fuzzy set theory for image despeckling. The intuitionistic fuzzy divergence function has been used to distinguish between edges and noise. To the best of the authors' knowledge, most of the studies on the multiplicative speckle noise removal process focus only on diffusion-based filters, and little attention has been paid to the study of fuzzy set theory. The proposed approach enjoys the benefits of both telegraph total variation equation and fuzzy edge detector, which is robust to noise and preserves image structural details. Moreover, we establish the existence and uniqueness of weak solutions of a regularized version of the present system using the Schauder fixed point theorem. With the proposed technique, despeckling is carried out on natural, real synthetic aperture radar, and real ultrasound images. The experimental results computed by the suggested method are reported, which are found better in terms of noise elimination and detail/edge preservation, concerning the existing approaches.</p>

Intelligent Deep Learning Based Disease Diagnosis Using Biomedical Tongue Images

The rapid development of biomedical imaging modalities led to its wide application in disease diagnosis. Tongue-based diagnostic procedures are proficient and non-invasive in nature to carry out secondary diagnostic processes ubiquitously. Traditionally, physicians examine the characteristics of tongue prior to decision-making. In this scenario, to get rid of qualitative aspects, tongue images can be quantitatively inspected for which a new disease diagnosis model is proposed. This model can reduce the physical harm made to the patients. Several tongue image analytical methodologies have been proposed earlier. However, there is a need exists to design an intelligent Deep Learning (DL) based disease diagnosis model. With this motivation, the current research article designs an Intelligent DL-based Disease Diagnosis method using Biomedical Tongue Images called IDLDD-BTI model. The proposed IDLDD-BTI model incorporates Fuzzy-based Adaptive Median Filtering (FADM) technique for noise removal process. Besides, SqueezeNet model is employed as a feature extractor in which the hyperparameters of SqueezeNet are tuned using Oppositional Glowworm Swarm Optimization (OGSO) algorithm. At last, Weighted Extreme Learning Machine (WELM) classifier is applied to allocate proper class labels for input tongue color images. The design of OGSO algorithm for SqueezeNet model shows the novelty of the work. To assess the enhanced diagnostic performance of the presented IDLDD-BTI technique, a series of simulations was conducted on benchmark dataset and the results were examined in terms of several measures. The resultant experimental values highlighted the supremacy of IDLDD-BTI model over other state-of-the-art methods.

Artificial Intelligence Enabled Apple Leaf Disease Classification for Precision Agriculture

Precision agriculture enables the recent technological advancements in farming sector to observe, measure, and analyze the requirements of individual fields and crops. The recent developments of computer vision and artificial intelligence (AI) techniques find a way for effective detection of plants, diseases, weeds, pests, etc. On the other hand, the detection of plant diseases, particularly apple leaf diseases using AI techniques can improve productivity and reduce crop loss. Besides, earlier and precise apple leaf disease detection can minimize the spread of the disease. Earlier works make use of traditional image processing techniques which cannot assure high detection rate on apple leaf diseases. With this motivation, this paper introduces a novel AI enabled apple leaf disease classification (AIE-ALDC) technique for precision agriculture. The proposed AIE-ALDC technique involves orientation based data augmentation and Gaussian filtering based noise removal processes. In addition, the AIE-ALDC technique includes a Capsule Network (CapsNet) based feature extractor to generate a helpful set of feature vectors. Moreover, water wave optimization (WWO) technique is employed as a hyperparameter optimizer of the CapsNet model. Finally, bidirectional long short term memory (BiLSTM) model is used as a classifier to determine the appropriate class labels of the apple leaf images. The design of AIE-ALDC technique incorporating the WWO based CapsNet model with BiLSTM classifier shows the novelty of the work. A wide range of experiments was performed to showcase the supremacy of the AIE-ALDC technique. The experimental results demonstrate the promising performance of the AIE-ALDC technique over the recent state of art methods.