Combination of DWT Variants and GLCM as a Feature for Brain Tumor Classification

Solid waste bin level detection using gray level co-occurrence matrix feature extraction approach

A brain tumor occurs when abnormal cells form within the brain. Many people suffer from brain tumor, and it is a serious and dangerous disease. The detection and classification of brain tumor is an important and difficult task in the medical field. The brain tumor detection technique in the MRI images is very significant in many symptomatic and cure applications. But, as the data in MRI images are of highquality, tumor detection and classification are very hard in this process. Medical imaging plays a major role in properly diagnosing the disease, wherein an essential part in detecting the tumor is image segmentation. The segmentation provides an automatic brain tumor detection technique in order to increase the precision of the yields while also decreasing the diagnosis time. The goal is to detect the tumor from the MRI images and extract the features from the segmented tumor and finally classify it. The image detection and classification process includes image acquisition, image pre-processing, denoising, image segmentation, feature extraction and classification. The input image is pre-processed using Weiner and median filtering and the noise is removed using Edge Adaptive Total Variation Denoising (EATVD) technique. Once the noise is removed from the image, it is used for segmentation process, where Mean Shift Clustering is used. The segmented tumor undergoes features extraction stage, where Gray Level Co- occurrence Matrix (GLCM) features are used. In the last stage, images are classified either as tumorous or non-tumorous. Classification is done using Genetic Algorithm Support Vector Machine (GA-SVM), Deep Learning with Convolutional Neural Network (CNN). Early detection of the tumor region can be achieved without much time lapse in the calculation by using this efficient classifier model. This system presents a prototype for detecting objects based on GA-SVM that classifies images and assesses whether the image is cancerous. While comparing the accuracy and computational time of these classifiers, CNN would provide high accuracy and GASVM with lesser simulation time. The simulation results obtained for brain tumor detection and analysis are done with minimum computational time and with reasonable accuracy. This proposed system is tested using SPL dataset, which consists of 20 cases with 40 image samples of T2 FLAIR weighted MRI image and implemented using MATLAB software.

Bridging the gap between micro- and macro-scales in medical imaging with textural analysis – A biological basis for CT radiomics classifiers?

The analysis of anti-nuclear antibodies in HEp-2 cells by Indirect Immunofluorescence (IIF) is considered a powerful, sensitive, and comprehensive test for auto-antibodies analysis for autoimmune diseases. The aim of this study is to explore the use of wavelet texture analysis for automated categorization of auto-antibodies into one of the six categories of immunofluorescent staining. Gray level co-occurrence matrix (GLCM) features were extracted over sub-bands obtained from multi-level wavelet decomposition. In this study, an attempt is also made to investigate effect of different wavelet bases and their superiority on spatial domain features on classification task at hand. A qualitative as well as quantitative comparison is done between GLCM features in wavelet domain and spatial domain. Discrete Meyer wavelet has been found to be the most discriminating for this classification task.

The lungs are a very important part of the human body, as they serve as a place for oxygen exchange. They have a very complex task and are susceptible to damage from the polluted air we breathe every day, which can lead to various diseases. Lung disease is a very common health problem that can be found in everyone, but there are still many people who do not pay attention to their lung health, making them vulnerable to lung disease. One of the methods used to detect lung disorders is by examining images obtained from X-rays. Image processing is one of the techniques that can also be used for lung disease identification and is most commonly used in medical images. Therefore, the purpose of this research is to implement image processing to determine the accuracy of lung disease identification using deep learning algorithms and the application of feature extraction. In this research, there are two experiments conducted consisting of the application of the classification method, namely Convolutional Neural Network and Gray Level Co-Occurrence Matrix feature extraction with CNN. The results show that the CNN model gets a precision of 0.92, recall of 0.92, f1-score of 0.92, and average accuracy of 0.92. The combination of the GLCM method with CNN produces a precision of 0.87, recall of 0.87, f1-score of 0.87, and average accuracy of 0.87. The results of this study indicate that the use of CNN in the lung disease classification model based on X-ray images is superior to the GLCM-CNN method.

A brain tumor is a mass or growth of abnormal cells in our brain. Many different types of brain tumors exist. Some brain tumors are noncancerous (benign), and some brain tumors are cancerous (malignant). Brain tumors can begin in your brain (primary brain tumors), or cancer can begin in other parts of your body and spread to your brain (secondary, or metastatic, brain tumors). Brain tumor treatment options depend on the type of brain tumor you have, as well as its size and location. The classification of brain tumors is performed by biopsy, which is not usually conducted before definitive brain surgery. The improvement of technology and machine learning can help radiologists in tumor diagnostics without invasive measures. A machine-learning algorithm that has achieved substantial results in image classification is the convolution neural network (CNN). It is predicted that the success of the obtained results will increase if the CNN method is supported by adding extra feature extraction methods and classify successfully brain tumor normal and abnormal image.

The abnormal or irregular growth of cells in regions of the human body that affects surrounding tissues is termed a tumor. Brain tumors are among the most dangerous and life-threatening types of tumors, arising from the abnormal growth of cells within the brain. However, existing detection methods often suffer from limitations, such as poor noise handling in MRI images, inaccurate segmentation, and low generalization across varying datasets. To resolve these difficulties, in this article, the convolutional-adaptive neuro fuzzy inference system (Conv-ANFIS) approach is devised for brain tumor detection from the magnetic resonance imaging (MRI) brain images. Conv-ANFIS is a combined model that merges a convolutional neural network (CNN) with an adaptive neuro-fuzzy inference system (ANFIS) and helps improve brain tumor detection accuracy. Initially, the de-noising of acquired input MRI images is done by applying a non-local means (NLM) filter. The skull portions are removed from the de-noised MRI image. Next, segmentation is performed by applying the structure correcting adversarial network (SCAN) to the image after skull removal. SCAN is a deep learning-based segmentation model designed for medical imaging. It ensures the accurate detection of tumors in MRI images. Subsequently, the features are mined from the segmented images by utilizing different feature extractors. Finally, the brain tumor is identified using the designed Conv-ANFIS model from the extracted feature vector. Furthermore, the effectiveness of Conv-ANFIS in detection is confirmed, achieving a recall of 92.31%, precision of 90.13%, and an F1-score of 91.21%, outperforming existing approaches.

Breast cancer is one of the main causes of death in women and ranks first in cancer cases in Indonesia. Therefore, an early detection and prevention of breast cancer is necessary, one of which is through mammography procedures. A machine learning classifier such as Support Vector Machines (SVM) could be used as an aid to the doctors and radiologist in diagnosing breast cancer from the mammogram images. The aim of this paper is to compare two feature extraction methods used in SVM, namely the Gray Level Co-Occurrence Matrix (GLCM) and first order with two kernels for each method, namely Gaussian and Polynomial. Classification using SVM method is carried out by testing several parameters such as the value of C, gamma, degree and varying the pixel spacing values ​​in GLCM, which usually in previous studies only used the default pixel spacing. The dataset consists of 500 mammogram images containing 250 benign and malignant images, respectively. This study is expected to find out the best method with the highest accuracy between these two texture feature extractions and and able to distinguish between benign and malignant classes correctly. The result achieved that Gray Level Co-Occurrence Matrix (GLCM) feature extraction method with both Gaussian and Polynomial kernel yields the best performance with an accuracy of 89%.

Pap smear is a digital image generated from the recording of cervical cancer cell preparation. Images generated are susceptible to errors due to the relatively small cell sizes and overlapping cell nuclei. Therefore, accurate Pap smear image analysis is essential to obtain the right information. This research compares nucleus segmentation and detection using Grey Level Co-occurrence Matrix (GLCM) features in two methods: Otsu and Polynomial. The tested data consisted of 400 images sourced from RepoMedUNM, a publicly accessible repository containing 2,346 images. Both methods were compared and evaluated to obtain the most accurate features. The research results showed that the average distance of the Otsu method was 6.6457, which was superior to the Polynomial method with a value of 6.6215. Distance refers to the distance between the nucleus detected by the Otsu and the Polynomial method. Distance is an important measure to assess how closely the detection results align with the actual nucleus positions. It indicates that the Polynomial method produces nucleus detections that are on average closer to the actual nucleus positions compared to the Otsu method. Consequently, this research can serve as a reference for further studies in developing new methods to enhance the accuracy of identification.

In this paper, we present an approach for human emotion description based on proposed algorithm combines the strengths of two methods of analyzing textures, the Discrete Wavelet Transform (DWT) and the Grey Level Co-occurrence Matrix (GLCM). The proposed algorithm is introduced to extract the texture features by combining (1) wavelet statistical features extracted from the approximation and detail regions of three level discrete wavelet transform decomposed images and (2) grey level co-occurrence matrix features extracted from original image, approximation and detail sub-bands of one level discrete wavelet transform decomposed images at different angles . Experimental results are presented using image database consists of a set of photograph face images describing some human emotions, these images were generated by using a digital video camera. The experimental results show that the proposed algorithm is effective and achieves high similarity ratios.

Accurate detection and classification of brain tumors are essential for timely diagnosis and effective treatment planning. This study presents an integrated framework leveraging both machine learning (ML) and deep learning (DL) models for brain tumor detection and classification using MRI images. Two publicly available datasets are utilized: one for binary classification (tumor vs. no tumor) and another for multiclass classification (glioma, meningioma, and pituitary tumors). Comprehensive preprocessing steps, including resizing, feature extraction using the Gray Level Co-occurrence Matrix (GLCM), and feature selection via Chi-square testing, were employed to optimize the dataset for modeling. Machine learning models such as Decision Trees, K-Nearest Neighbors (KNN), Support Vector Machines (SVM), and AdaBoost were compared with deep learning architectures like Convolutional Neural Networks (CNNs) and the pre-trained VGG16 model. Hyperparameter optimization techniques, including grid search and the Adam optimizer, were used to enhance model performance. The models were evaluated using metrics such as accuracy, precision, recall, F1-score, Mean Squared Error (MSE), and Mean Absolute Error (MAE). Results indicate that the VGG16 model consistently outperformed other approaches, achieving high validation accuracy. This study highlights the potential of integrating ML and DL techniques for accurate and efficient brain tumor detection and classification, offering valuable tools for medical diagnostics.

According to the World Health Organization, cancer is the second most common cause of death worldwide (WHO). Death from cancer may sometimes be avoided through early diagnosis, although this is not always practicable. A tumour, unlike cancer, may be benign, pre-cancerous, or malignant. Contrary to malignant tumours, benign tumours may frequently be surgically removed and seldom metastasize to other organs or tissues. The method most often used to differentiate between various types of tumours is magnetic resonance imaging (MRI) (MRI). But because it depends on human subjectivity, it could be challenging for one person to see a lot of data. The radiologist's ability to identify brain tumours early mostly depends on their level of training. Before determining whether the tumour is benign or malignant, the diagnostic process for it could not be finished. A biopsy is often carried out to determine if the tissue is benign or cancerous. The biopsy of a brain tumour is often delayed until the last brain operation, in contrast to tumours discovered elsewhere in the body. An efficient diagnostics tool for tumour segmentation and classification from MRI images is crucial to obtaining accurate diagnoses, avoiding surgery, and eliminating subjectivity. The classification and segmentation of the tumour part of the MRI brain picture is the goal of this study. Prior to feature extraction in the first study, pre-processing is done using the Gray Level Co-occurrence Matrix (GLCM). Based on these features, the data is then separated into normal, glioma, meningioma, and pituitary groups. In prior studies, classification was handled by traditional Deep Neural Networks (DNN), with the best weights selected using optimization methods. Accordingly, a recent study recommends including Long Short-Term Memory based on Recurrent Neural Networks (RNN) (LSTM). The Local Direction Ternary Pattern (LDTP), the Gray Level Co-occurrence Matrix (GLCM), and the Le-Net features are all combined in the proposed Hybrid GLCM-LDTP-Le-Net Feature extraction approach. The hybrid parameters produced by the feature extraction process and the brain MRI data are used by the RNN-LSTM model to categorise the data as benign or malignant. By removing mixed features from the input data, the suggested hybrid feature extraction improves the learnt models' accuracy. The accuracy of 98.99 % of the suggested hybrid feature extraction approach was higher than the current CNN-based model methodologies.

Automatic feature extraction and detection of plant leaf disease using GLCM features and convolutional neural networks

Cluster of tissue that influence the normal tissue by moderate expansion of irregular cells is known as Brain tumour. It happens when cell get anomalous development inside the brain and this remains the primary reason in increasing the fatality rate among humans. Among a wide range of cancers, brain tumour is incredibly serious and to avoid life threats of an individual, quick diagnosing and treatment must be given. Identifying these cells is a problematic issue, due to formation of tumour cells. It is exceptionally crucial to classify brain tumour from MRI image. In our proposed work, MRI images retrieved by utilizing Content based image retrieval technique are taken as input for classification. To achieve accuracy in classification and for efficient segmentation, pre-processing is carried for color conversion, noise reduction and resizing. Segmentation of tumour cells is done by Expectation and Maximization technique to know about region of affected cells present in that segmented area. This is trailed by statistical and size-based feature extraction by Gray Level Co-Occurrence Matrix from segmented images. The features extracted from segmented portion will be trained to analyze the presence of tumour in given MR images. The brain tumor classification, done in MATLAB environment allows localizing a mass of abnormal cells in a slice of MRI image using SVM Classifier, which is quickest method and furthermore give the great accuracy in classification. Experimental results accomplished accuracy of 100% in distinguishing the tissues as normal or abnormal from MR images exhibiting the viability of proposed method.

Early and precise discovery of brain tumors in MRI data is serious for effective analysis and therapy planning. This study presents an automated methodology combining Discrete Wavelet Transform (DWT) with Convolutional Neural Networks (CNNs) for tumor localization, discovery, and cataloguing. The proposed system integrates advanced image processing techniques through a pre-processing, feature removal using a 2D - DWT combined with Grey Level Co-occurrence Matrix (GLCM), and classification with Hidden layer Convolutional Neural Networks (HL-CNNs). This hybrid approach addresses challenges such as noise in MRI scans, enabling accurate differentiation between tumorous and non-tumorous MRIs and further classifying tumors as benign or malignant. The system demonstrates robust performance, achieving high diagnostic accuracy and reliability when tested on Magnetic Resonance Images (MRI) from the Harvard Dataset. By automating the screening process, this methodology highlights the potential of DWT and HL- CNNs to advance brain tumor characterization, offering significant improvements over conventional manual methods.