Brain image clustering by wavelet energy and CBSSO optimization algorithm

Previously, the diagnosis of brain abnormality was significantly important in the saving of social and hospital resources. Wavelet energy is known as an effective feature detection which has great efficiency in different utilities. This paper suggests a new method based on wavelet energy to automatically classify magnetic resonance imaging (MRI) brain images into two groups (normal and abnormal), utilizing support vector machine (SVM) classification based on chaotic binary shark smell optimization (CBSSO) to optimize the SVM weights. The results of the suggested CBSSO-based KSVM are compared favorably to several other methods in terms of better sensitivity and authenticity. The proposed CAD system can additionally be utilized to categorize the images with various pathological conditions, types, and illness modes

Comparative Study on Medical Image Classification Techniques

This brief study compares the proposed RGSA algorithm with other recent methods by several experiments to indicate that proposed 3DGLCM and SGLDM with SVM classifier is more efficient and accurate. The accuracy results of this study imply how well their experimental results were found to give more accurate results of classifying tumors. The center of interest for this study was made on supervised classification approaches on 2D MRI images of brain tumors. This paper gives the comparative study of various approaches that was used to identify the tumor cells with classifiers

DETEKSI KANKER OTAK PADA DATA MRI MELALUI JARINGAN SYARAF TIRUAN DENGAN EKSTRAKSI FITUR DISCRETE WAVELET TRANSFORM

Secaraumum, kankerotakadalahsebuahpenyakit yang timbuldaripembelahansel-selotak yang tidakwajardandapatmenyebarkeseluruhbagianotak yang lain. Diagnosakankerotakdapatdilakukanmelaluipemeriksaanmagnetic resonance imaging (MRI). Magnetic resonance imaging (MRI) merupakanpemeriksaandenganmenggunakanmedan magnet danresonansigetaranterhadapinti atom hidrogen yang dapatmenghasilkanrekamangambar organ manusia yang biasadigunakandokteruntukmendeteksidanmendiagnosapasiennya. Terkadang, seorangdoktermengalamikesulitandalammendiagnosapasienkankerotakdikarenakanperbedaanpersepsidananalisissaatmelihathasilcitra MRI.Discrete wavelet transform(DWT) merupakanmetodeekstraksifitur yang berfungsiuntukmencaricirikhususatauciriobjek yang terdapatpadasebuahcitra MRI. Padapenelitianiniakandibahasmetodepengolahancitra digital sebagaimetodeperbaikandananalisisctirasertajaringansyaraftiruanbackpropagationyang digunakansebagaimetodepengenalandanklasifikasicitra MRI otakmanusia. Hasilklasifikasiiniadalah MRI normal dan abnormal. Tingkat akurasi yang paling baik pada sistem deteksi kanker otak yang dirancang adalah 100% untuk data training dan 80% untuk data testing

MELASTOMA MALABATHRICUM L. EXTRACTS-BASED INDICATOR FOR MONITORING SHRIMP FRESHNESS INTEGRATED WITH CLASSIFICATION TECHNOLOGY USING NEAREST NEIGHBOURS ALGORITHM

As a maritime country, shrimp commodity production in Indonesia is very high and continues to increase. However, because shrimp is a perishable food, we need a detection device. This is because conventional methods that are widely used by the community in detecting freshness of shrimp are only based on the smell. Of course, this is a problem when shrimp are packed in closed containers. In this paper, a method for detecting shrimp is proposed using the Melastoma malabathricum L. - based label indicator. The high content of flavonoids in the extracts allows the changing the colour of the label from red to grey due to the interaction between the label with the OH- group that arises from the shrimp spoilage process. The colour that appears on the label indicator will correlate with the level of shrimp freshness. By increasing detection effectiveness, the classification is performed using the nearest-neighbours algorithm, which is equipped with an image processing mechanism in the form of colour quantization. There are four classifications used to express the quality of shrimp, namely "acceptable," "just acceptable," "unacceptable," and "more unacceptable." The accuracy of applying this method is 71.9%, with the majority of detection errors occurring in the "acceptable" class. Based on these results, it can be stated that the label indicators prepared in this study are very promising to be developed into intelligent packaging components

Classification of MRI Brain images using GLCM, Neural Network, Fuzzy Logic & Genetic Algorithm

Detection of Brain abnormality could be a vital and crucial task in medical field. Resonance Imaging Brain image detection method offers the knowledge of the various abnormalities in Brain. This helps the doctors in treatment coming up with. Within the previous work, within the field of medical image process several scientist and soft computing techniques have totally different strategies like totally automatic and semiautomatic. During this projected technique, 2 totally different classification strategies are used along for the classification of magnetic resonance imaging Brain pictures. Those classification strategies square measure Neural Network and fuzzy logic. With this projected hybrid technique Genetic algorithmic program is employed for the optimization. Projected technique consists of various stages. Knowledge assortment through numerous hospitals or repository sites and convert original data pictures into gray scale image. Gray Level Co-occurrence Matrix technique is employed for the extraction of the options from the gray scale image. Optimization technique Genetic algorithmic program is especially used for reducing the options that square measure extracted by GLCM for simple classification and reducing the convergence time or computation time. there\'s a hybrid classifier is employed for classification of magnetic resonance imaging brain pictures specifically Neural and Fuzzy classifier. DOI: 10.17762/ijritcc2321-8169.15060

POLA PEMETAAN PENYEBARAN PENYAKIT DEMAM BERDARAH PADA WILAYAH KOTA PALEMBANG

Palembang region consists of 16 districts, an area that is separated by a large river that divides into two regionsand Ulu Ilir, Palembang, besides most of the region is also an area of swamps. Environmental conditions is very large influence on the spread of dengue fever. The problem faced is the difficulty of mapping the spread of dengue fever that has slowed the anticipation and prevention of dengue fever cases. Needed a pattern that maps the spread of dengue fever by location (region), time, and season. To prevent Extraordinary Events (KLB) and monitor the spread of dengue fever in the region of Palembang in this study carried out to map the spread of dengue fever in the region of Palembang by using the nearest neighbor method

Brain Tumor Detection and Multi Classification Using GNB-Based Machine Learning Approach

In an abnormal tissue called a brain tumor, the cells of the tumor reproduce quickly. if no control over tumor cell growth. The difficulties involved in identifying and treating brain tumors Machine learning is the most technologically sophisticated tool for classification and detection, implementing reliable state-of-the-art A.I. as well as neural network classification techniques, the use of this technology in early diagnosis detection of brain tumors can be accomplished successfully. it is well known that the segmentation method is capable of helping simply destroy the brain's abnormal tumor regions In order to segment and categorize brain tumors, this study suggests a multimodal approach involving machine learning and medical assistance. Noise can be seen in MRI images. To make the method for eliminating noise from images easier, a geometric mean is used later. The algorithms used to segment an image into smaller pieces are fuzzy c-means algorithms. Detection of a specific area of interest is made simpler by segmentation. The dimension reduction procedure is carried out using the GLCM. Photographic features are extracted using the GLCM algorithm. Then, using a variety of ML techniques, like as CNN, ANN, SVM, Gaussian NB, and Adaptive Boosting, the photos are categorized. The Gaussian NB method performs more effectively with regard to the identification and classification of brain tumors. The plasterwork work achieved 98.80 percent accuracy using GNB, RBF SVM