Image Pre-processing Stage Research Articles

FRRSA: Fractional Remora Reptile Search Algorithm-based LeNet for rice leaf disease classification

ABSTRACT Rice is considered as a chief food in most of the areas across India, simultaneously rice plants are infected by diverse diseases strongly, which causes vital losses in the agricultural field. So, earlier and more precise prediction of diverse rice crop diseases becomes a main challenge to farmers and research persons. Hence, a Fractional Remora Reptile search algorithm-LeNet (FRRSA-LeNet) is proposed for rice leaf disease classification. At first, the input leaf image is passed to the image pre-processing stage. In image pre-processing, a bilateral filter is utilized for the quality improvement of an image. Afterwards, segmentation is carried out, where the GrabCut algorithm is employed for segmenting disease-affected areas. Then, the image augmentation phase is conducted and in the feature extraction, CNN features and statistical features are extracted for the following process. Lastly, rice leaf disease classification is conducted utilizing LeNet, which is tuned by the proposed FRRSA. Moreover, FRRSA is introduced by assimilating Fractional Calculus (FC), Remora Optimization Algorithm (ROA), and Reptile Search Algorithm (RSA). FRRSA-LeNet acquired maximal testing accuracy of 0.903, TPR of 0.883, TNR of 0.904, and MSE of 0.800. The proposed model accurately identifies rice leaf diseases, enhancing the overall effectiveness of the classification process.

Fingerprint Identification for Attendance Using Euclidean Distance and Manhattan Distance

Attendance is an action to confirm that someone is present at the office, school, or event. The use of attendance in an agency or company is really important as it can improve the level of discipline and productivity. However, the traditional way of doing attendance is considered less effective, less secure, and more difficult to organize. Therefore, a modern attendance system that utilizes fingerprints can be the right solution, especially because every fingerprint is unique. In this research, we focus on designing a fingerprint identification system for attendance purposes by using two distance measure methods, namely Euclidean Distance and Manhattan Distance. The dataset used in the research contains 111 fingerprint images with 90 images for training the designed fingerprint identification system and the remaining 21 images for testing the system. Each fingerprint image has undergone image pre-processing stage before being used. We compare Euclidean Distance and Manhattan Distance based on their performances in identifying fingerprint. From the test results, the fingerprint identification accuracy obtained using Euclidean Distance is 76.19%, while the accuracy obtained using Manhattan Distance is 71.43%. In general, both algorithms succeed in providing the correct identification results. This proves that Euclidean Distance and Manhattan Distance can be utilized for fingerprint identification purposes.

Deep Learning Approach for Paddy Field Detection Using Labeled Aerial Images: The Case of Detecting and Staging Paddy Fields in Central and Southern Taiwan

Detecting and mapping paddy fields in Taiwan’s agriculture is crucial for managing agricultural production, predicting yields, and assessing damages. Although researchers at the Taiwan Agricultural Research Institute currently use site surveys to identify rice planting areas, this method is time-consuming. This study aimed to determine the optimal band combinations and vegetation index for accurately detecting paddy fields during various phenological stages. Additionally, the Mask RCNN instance segmentation model in the ArcGIS Pro software was employed to enhance the effectiveness of detecting and segmenting paddy fields in aerial images. This study utilized aerial images collected from 2018 to 2019 covering Changhua, Yunlin, Chiayi, and Tainan in central and southern Taiwan, with a label file comprising four categories of rice growing, ripening, harvested stage, and other crops. To create different image datasets, the image pre-processing stage involved modifying band information using different vegetation indices, including NDVI, CMFI, DVI, RVI, and GRVI. The resolution of the training image chips was cropped to 550 by 550 pixels. After the model training process, the study found that the ResNet-50 backbone performed better than the ResNet-101, and the RGB + DVI image dataset achieved the highest mean average precision of 74.01%. In addition, the model trained on the RGB + CMFI image dataset was recommended for detecting paddy fields in the rice growing stage, RGB + NIR for the rice ripening stage, and RGB + GRVI for the rice harvested stage. These models exhibit Dice coefficients of 79.59%, 89.71%, and 87.94%, respectively. The detection and segmentation results can improve the efficiency of rice production management by using different band combinations according to different rice phenological stages. Furthermore, this method can be applied to large-scale detection of other crops, improving land use survey efficiency and reducing the burden on researchers.

Image processing and machine learning‐based bone fracture detection and classification using X‐ray images

AbstractOne of the most important problems in orthopedics is undiagnosed or misdiagnosed bone fractures. This can lead to patients receiving an incorrect diagnosis or treatment, which can result in a longer treatment period. In this study, fracture detection and classification are performed using various machine learning techniques using of a dataset containing various bones (normal and fractured). Firstly, the X‐ray images obtained are subjected to image preprocessing stages and are prepared for the feature extraction stage. Then, in addition to the Canny and Sobel edge detection methods used in the image processing stage, feature extraction of X‐ray images is performed with the help of Houhg line detection and Harris corner detector. The data obtained by performing feature extraction is given to 12 different machine learning classifiers and the results are presented. Setting hyperparameters for classifiers is done by the grid search method, and the study is tested for 10‐fold cross‐validation. Classifier results are presented comparatively as accuracy, training time, and testing time, and linear discriminant analysis (LDA) reaches the highest accuracy rate with 88.67% and 0.89 AUC. The proposed computer‐aided diagnosis system (CAD) will reduce the burden on physicians by identifying fractures with high accuracy.

Reconstruction and Visualization of 5μm Sectional Coronal Views for Macula Vasculature in OptoVue OCTA

A Computerized Medical Image Processing (CMIP) method is proposed to address the current challenges of optical coherence tomography angiography (OCTA): (1) the need for observing the macula vasculature concerning natural curvature of the macula region; (2) the need for generating OCTA frames at successive small depths in all macula layers; and (3) the need for enhancing the visibility of blood vessels, particularly below the outer retina region. The proposed CMIP method involves image preprocessing, reconstruction, and enhancement stages. Twenty subjects were obtained from the OCTA500 dataset, which was obtained from the OptoVue OCTA machine. The 20 subjects comprise the two OCTA fields of view (FOV), right and left eyes (OD and OS), and five common macula disorders. The sequential enface OCTA images at 5μm macula depths were displayed. The presentation of the macula vasculature was enhanced at all depths. The resulting new ophthalmic views enable: (1) avoiding the superimposition of macula vasculature into a projection map; (2) enhancing the OCTA presentation of blood vessels; and (3) inspecting the macula’s 3D oval-shaped. The proposed CMIP method can generate sectional macula coronal views (MCV) for every 5μm depth, clarifying the macula’s curvature in a way that has not been presented in OCTA machines. Therefore, “tracking” the 3D propagation of the blood vessel network at all macula depths becomes possible. Furthermore, the blood vessels’ display at all macula depths, including the deep choroid layers, is enhanced. The method yields futuristic ophthalmic advantages that would allow the physician to precisely inspect the 3D localization and diffusion of the macula disorders. The method is invariant to the OCTA’s FOVs, macula disorder, and OD or OS eye.

Dataset Gambar Wajah untuk Analisis Personal Identification

In today's era, which is supported by technological advances, personal assistance through the face can be carried out by sophisticated machines and robots. One of its applications is personal identification using data mining. But before conducting data training and data classification, it is necessary to carry out the process of data collection and data cleaning or data pre-processing. Currently the face dataset for personal identification at the Caltex Riau Polytechnic in particular is still in the form of raw data, namely in the form of a collection of images that have not been pre-processed. Therefore, this research will perform image preprocessing to clean up the image data that has been collected so that the data can become a cleaner source of information and can be used at a later stage. The data used in this study are image data or photos of Caltex Riau Polytechnic students. At the facial image pre-processing stage using the OpenCV library using the Python programming language. Images collected by 500 students for 5 students. The results of this study are the personal identification dataset of Caltex Riau Polytechnic students consisting of 280 images that have successfully passed the stages of grayscaling, cropping, resizing and Normalization. This dataset is stored in the file data_norm.npz. White box testing is carried out to determine the accuracy of the application of the image pre-processing stage with the test results stating that all functional basis paths applied are in accordance with the cyclometic complexity and its independent path.

Dexterous Identification of Carcinoma through ColoRectalCADx with Dichotomous Fusion CNN and UNet Semantic Segmentation.

Human colorectal disorders in the digestive tract are recognized by reference colonoscopy. The current system recognizes cancer through a three-stage system that utilizes two sets of colonoscopy data. However, identifying polyps by visualization has not been addressed. The proposed system is a five-stage system called ColoRectalCADx, which provides three publicly accessible datasets as input data for cancer detection. The three main datasets are CVC Clinic DB, Kvasir2, and Hyper Kvasir. After the image preprocessing stages, system experiments were performed with the seven prominent convolutional neural networks (CNNs) (end-to-end) and nine fusion CNN models to extract the spatial features. Afterwards, the end-to-end CNN and fusion features are executed. These features are derived from Discrete Wavelet Transform (DWT) and Vector Support Machine (SVM) classification, that was used to retrieve time and spatial frequency features. Experimentally, the results were obtained for five stages. For each of the three datasets, from stage 1 to stage 3, end-to-end CNN, DenseNet-201 obtained the best testing accuracy (98%, 87%, 84%), ((98%, 97%), (87%, 87%), (84%, 84%)), ((99.03%, 99%), (88.45%, 88%), (83.61%, 84%)). For each of the three datasets, from stage 2, CNN DaRD-22 fusion obtained the optimal test accuracy ((93%, 97%) (82%, 84%), (69%, 57%)). And for stage 4, ADaRDEV2-22 fusion achieved the best test accuracy ((95.73%, 94%), (81.20%, 81%), (72.56%, 58%)). For the input image segmentation datasets CVC Clinc-Seg, KvasirSeg, and Hyper Kvasir, malignant polyps were identified with the UNet CNN model. Here, the loss score datasets (CVC clinic DB was 0.7842, Kvasir2 was 0.6977, and Hyper Kvasir was 0.6910) were obtained.

Ripeness Identification of Chayote Fruits using HSI and LBP Feature Extraction with KNN Classification

This study aims to build a system to identify the ripeness level of chayote that can be done easily and without damaging the quality of the chayote. This study employs digital image processing technology using Hue Saturation Intensity color feature extraction and texture feature extraction of Local Binary Pattern with K-Nearest Neighbor classification so that the process of identifying the ripeness level of chayote will be easier and more effective. This study uses 100 image datasets and is carried out by taking photos of chayote. The stages in this study include the input of chayote images followed by the image pre-processing stage. Next is feature extraction which is divided into three scenarios, namely HSI feature extraction, LBP feature extraction and a combination of the two feature extractions. The final stage is to classify objects that are closest to the object being tested using the KNN method. By determining the value of K in the KNN classification method, the results show that the use of the Chebyshev distance calculation model in LBP feature extraction with K = 5 is a test that has the best accuracy of 90%.

Finger Print Authentication on Web-Services One Step to Next Security

The system that automatically identifies the anthropometric fingerprint is one of the systems that interact directly with the user, which every day will be provided with a diverse database. This requires the system to be optimized to handle the process to meet the needs of users such as fast processing time, almost absolute accuracy, no errors in the real process. Therefore, in this paper, we propose the application of machine learning methods to develop fingerprint classification algorithms based on the singularity feature. The goal of the paper is to reduce the number of comparisons in automatic fingerprint recognition systems with large databases. The combination of using computer vision algorithms in the image pre-processing stage increases the calculation time, improves the quality of the input images, making the process of feature extraction highly effective and the classification process fast and accurate. The classification results on 3 datasets with the criteria for Precision, Recall, Accuracy evaluation and ROC analysis of algorithms show that the Random Forest (RF) algorithm has the best accuracy (≥96.75%) on all 3 databases, Support Vector Machine (SVM) has the best results (≥95.5%).

Image classification using convolutional neural network with wavelet domain inputs

Commonly used convolutional neural networks (CNNs) usually compress high-resolution input images. Although it reduces the computation requirements into a reasonable range, the downsampling operation causes information loss, which affects the accuracy of image classification. How to adopt high-resolution image inputs to improve the quality of input information and thus improve the classification accuracy without changing the overall structure of the pre-defined CNN model or increasing the model parameters is an important issue. Here, a CNN model with wavelet domain inputs is proposed to provide a solving scheme. Specifically, the proposed method applies wavelet packet transform or dual-tree complex wavelet transform to extract information from input images with higher resolutions in the image pre-processing stage. Some subband image channels are selected as the inputs of conventional CNNs where the first several convolutional layers are removed, so that the networks directly learn in the wavelet domain. Experiment results on the Caltech-256 dataset and the Describable Textures Dataset with the ResNet-50 show that the classification accuracy of our method can have a maximum improvement of 2.15% and 10.26%, respectively. These validate the effectiveness of our proposed scheme. This code is publicly available at https://github.com/BeBeBerr/wavelet-cnn.

Autonomous Unmanned Aerial Vehicles Based Decision Support System for Weed Management

Recently, autonomous systems become a hot research topic among industrialists and academicians due to their applicability in different domains such as healthcare, agriculture, industrial automation, etc. Among the interesting applications of autonomous systems, their applicability in agricultural sector becomes significant. Autonomous unmanned aerial vehicles (UAVs) can be used for suitable site-specific weed management (SSWM) to improve crop productivity. In spite of substantial advancements in UAV based data collection systems, automated weed detection still remains a tedious task owing to the high resemblance of weeds to the crops. The recently developed deep learning (DL) models have exhibited effective performance in several data classification problems. In this aspect, this paper focuses on the design of autonomous UAVs with decision support system for weed management (AUAV-DSSWM) technique. The proposed AUAV-DSSWM technique intends to identify the weeds by the use of UAV images acquired from the target area. Besides, the AUAV-DSSWM technique primarily performs image acquisition and image pre-processing stages. Moreover, the Adam optimizer with You Only Look Once Object Detector-(YOLOv3) model is applied for the detection of weeds. For the effective classification of weeds and crops, the poor and rich optimization (PRO) algorithm with softmax layer is applied. The design of Adam optimizer and PRO algorithm for the parameter tuning process results in enhanced weed detection performance. A wide range of simulations take place on UAV images and the experimental results exhibit the promising performance of the AUAV-DSSWM technique over the other recent techniques with the of 99.23%.

Development of a chickpea disease detection and classification model using deep learning

Ethiopia is the largest producer of chickpeas in Africa. Crop production and yield in Ethiopia is greatly affected by plant diseases which cause loss of agricultural products every year. One of these plant diseases is chickpea disease which is a fungal disease. Ascochyta blight and Fusarium wilt are the most common chickpea diseases in Ethiopia that affect crop production quality and quantity. The identification of these diseases requires experienced experts or systems. Although numerous methods have been previously adopted in literature, deep learning (DL) is suggested as an efficient approach for disease recognition and classification since it can automatically learn features from the input image. In this paper, a chickpea disease detection model was developed using deep learning techniques by combining Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) for feature extraction and Softmax for classification. To develop the proposed model, various image preprocessing stages such as image resizing, normalization, and noise filtering using a combination of Gaussian filter (GF) and Median filter (MF) were performed. To prevent the problem of overfitting, augmentation was applied, while to train and test the effectiveness of the developed model, 8391 images were used. From the acquired images, 80% of the dataset was used for training, 20% of the dataset was used for testing and out of the 80% training data, 20% was used for validation. The proposed CNN-LSTM performed well in identifying chickpea disease, with an accuracy of 92.55%. According to the study's findings, the proposed CNN-LSTM outperforms existing methods.

Convolutional Neural Network Based Intelligent Handwritten Document Recognition

This paper presents a handwritten document recognition system based on the convolutional neural network technique. In today’s world, handwritten document recognition is rapidly attaining the attention of researchers due to its promising behavior as assisting technology for visually impaired users. This technology is also helpful for the automatic data entry system. In the proposed system prepared a dataset of English language handwritten character images. The proposed system has been trained for the large set of sample data and tested on the sample images of user-defined handwritten documents. In this research, multiple experiments get very worthy recognition results. The proposed system will first perform image pre-processing stages to prepare data for training using a convolutional neural network. After this processing, the input document is segmented using line, word and character segmentation. The proposed system get the accuracy during the character segmentation up to 86%. Then these segmented characters are sent to a convolutional neural network for their recognition. The recognition and segmentation technique proposed in this paper is providing the most acceptable accurate results on a given dataset. The proposed work approaches to the accuracy of the result during convolutional neural network training up to 93%, and for validation that accuracy slightly decreases with 90.42%.

RFARN: Retinal vessel segmentation based on reverse fusion attention residual network.

Accurate segmentation of retinal vessels is critical to the mechanism, diagnosis, and treatment of many ocular pathologies. Due to the poor contrast and inhomogeneous background of fundus imaging and the complex structure of retinal fundus images, this makes accurate segmentation of blood vessels from retinal images still challenging. In this paper, we propose an effective framework for retinal vascular segmentation, which is innovative mainly in the retinal image pre-processing stage and segmentation stage. First, we perform image enhancement on three publicly available fundus datasets based on the multiscale retinex with color restoration (MSRCR) method, which effectively suppresses noise and highlights the vessel structure creating a good basis for the segmentation phase. The processed fundus images are then fed into an effective Reverse Fusion Attention Residual Network (RFARN) for training to achieve more accurate retinal vessel segmentation. In the RFARN, we use Reverse Channel Attention Module (RCAM) and Reverse Spatial Attention Module (RSAM) to highlight the shallow details of the channel and spatial dimensions. And RCAM and RSAM are used to achieve effective fusion of deep local features with shallow global features to ensure the continuity and integrity of the segmented vessels. In the experimental results for the DRIVE, STARE and CHASE datasets, the evaluation metrics were 0.9712, 0.9822 and 0.9780 for accuracy (Acc), 0.8788, 0.8874 and 0.8352 for sensitivity (Se), 0.9803, 0.9891 and 0.9890 for specificity (Sp), area under the ROC curve(AUC) was 0.9910, 0.9952 and 0.9904, and the F1-Score was 0.8453, 0.8707 and 0.8185. In comparison with existing retinal image segmentation methods, e.g. UNet, R2UNet, DUNet, HAnet, Sine-Net, FANet, etc., our method in three fundus datasets achieved better vessel segmentation performance and results.

A novel approach for fracture skeleton extraction from rock surface images

The complexity and diversity of information on rock mass surfaces leads to disparities in many image properties among different rock mass surface images, including image brightness, color, contrast and pixel distribution. As a result, there is currently no unified automatic approach for extracting the rock fracture skeleton from the images. In this paper, according to common features of rock surface images and curvilinearity of fractures which are set as the extraction targets, a dark region curvilinear structure enhancement (DRCSE) algorithm for automatic rock fracture skeleton extraction is proposed. This algorithm mainly relies on the curvilinear structure enhancement filter Frangi in image pre-processing stage. Through multiple image processing, dark region contrast of rock surface images is enhanced while noise is reduced. After this process, the effect of Frangi filtering can be significantly improved. Particularly, by using Homomorphic filtering coupled Gamma transforming (HCG) to enhance dark region contrast, a new edge-entropy (EE) index is proposed to describe the contrast-enhanced image quality. Particle swarm optimization (PSO) is utilized to calculate the value of Gamma transforming parameters that maximize the EE index. The test results show that it has great advantages compared to traditional methods for rock fracture skeleton extraction.

Analysis of Crop disease detection with SVM, KNN and Random forest classification

Due to an uneven climatic condition crops are being affected which leads to decrease in agriculture yield. It greatly affects global agricultural economy. However, the condition becomes more worse when diseases are identified in crops. Agriculture plays a vital role in every country’s economy. Thus, there is a need to identify the crop disease before it is visible on a crop so that disease can be avoided by using appropriate measures. The traditional way of identifying a crop disease is through observation by naked eyes. But as it requires large number of experts and continuous monitoring of crop it will be costly for large fields. Hence, an automatic system is required which can not only examine the crops to detect disease but also can classify the type of disease on crops. The proposed system determines disease from an input image. The input image has to go through following stages: Image Acquisition, Image pre-processing, Image segmentation, Feature Extraction, and Classification in order to determine diseased crop and accordingly provides remedy for that disease. Infected crop image is taken as input in Image Acquisition stage. In Image pre-processing stage noise is removed from the input image by applying gaussian blur filter and non-local means denoising algorithm. Also, the background of image is eliminated using Thresholding algorithm. To extract Region of Interest (ROI) i.e. infected region from input image K-means Clustering algorithm is used. Then color, texture and shape features are extracted from ROI and features are further send to the classification stage. Three different classification algorithms namely Support Vector Machine (SVM), K-Nearest Neighbors (KNN) and Random Forest are implemented for classification out of which Support Vector Machine Algorithm is found to be best in terms of accuracy. Hence, classification is carried out by using Multivariate Support Vector Machine algorithm which detect disease present in crop accurately. In this way, the proposed system detects a disease from the given input image.

RETRACTED ARTICLE: A novel technique of leaf disease classification with encryption techniques for secured cloud data service

In recent times, the effective use of leaf disease detection is explored by numerous researches. Yet there is always a room for improved research analysis. An improved model for development is linked with imaging platforms. Hence there is a need for development of effective method of leaf disease detection combined with the encryption and decryption techniques. This provides security in cloud data service processing. This proposed research work includes image acquisition, pre-processing, segmentation, extraction, and classification stage. Outline of leaf is utilized to perceive the infections in leaves. The initial step is preprocessing then proposed segmentation depending on threshold segmentation with regression texture is performed. The subsequent stage is the extraction of the functionality, utilizing the gray level co-occurrence matrix tool. The third stage is classification using combined discrete wavelet transform-support vector machines is utilized. The final stage is the use of tiny encryption algorithm to encrypt data. The end encrypted output will be stored in the cloud. This technique is used to characterize the disease on the leaf. The simulations are applied on MATLAB. The research work gives accuracy of 98.2%. Sensitivity—96.7%, specificity—95.6% and precision—98.4% are obtained by proposed segmentation. The time for evaluation was found to be less than 23 s. Results of the experiment show the proposed efficiency of the research work as compared to other methods of detection.

Vision System for Automatic On-Tree Kiwifruit Counting and Yield Estimation

Yield estimation is an essential preharvest practice among most large-scale farming companies, since it enables the predetermination of essential logistics to be allocated (i.e., transportation means, supplies, labor force, among others). An overestimation may thus incur further costs, whereas an underestimation entails potential crop waste. More interestingly, an accurate yield estimation enables stakeholders to better place themselves in the market. Yet, computer-aided precision farming is set to play a pivotal role in this respect. Kiwifruit represents a major produce in several countries (e.g., Italy, China, New and Zealand). However, up to date, the relevant literature remains short of a complete as well as automatic system for kiwifruit yield estimation. In this paper, we present a fully automatic and noninvasive computer vision system for kiwifruit yield estimation across a given orchard. It consists mainly of an optical sensor mounted on a minitractor that surveys the orchard of interest at a low pace. Afterwards, the acquired images are fed to a pipeline that incorporates image preprocessing, stitching, and fruit counting stages and outputs an estimated fruit count and yield estimation. Experimental results conducted on two large kiwifruit orchards confirm a high plausibility (i.e., errors of 6% and 15%) of the proposed system. The proposed yield estimation solution has been in commercial use for about 2 years. With respect to the traditional manual yield estimation carried out by kiwifruit companies, it was demonstrated to save a significant amount of time and cut down on estimation errors, especially when speaking of large-scale farming.

Counter-propagation Neural Network for Brain Tumor Classification

Brain tumor is a condition in which abnormal cells grow unnaturally in the brain. Depending on the size and type, the abnormal cells called tumors can be life-threatening if the patient does not take immediate treatment. The cause of tumor growth in the brain is the presence of risk factors such as family history and ionization radiation. Patients with brain tumors will experience several symptoms of a headache, nausea, memory loss, and changes in vision, speech, and hearing. Detection of brain tumors can be performed with the help of the medical device of Magnetic Resonance Imaging (MRI) Scan. Through the image of MRI Scan results, radiology specialists will interpret and analyze the brain condition. However, analysis and conclusions for this matter take a long period of time. Therefore, a method is required to classify the brain tumors through MRI images automatically. The method used in this research is Counter-propagation Neural Network. Prior to classification, the brain’s MRI image will be used as the input for the image pre-processing stage then go through the segmentation and feature extraction processes. Based on the test, it can be concluded that the proposed method can identify brain tumors with an accuracy of 92.5%.

Neural Networks in Classifying Medical Imaging: Liver Cancer

Recent surveys show an increase in the rate of number of people dying due to late diagnosis of liver cancer. The cancerous cells need to be diagnosed in an efficient manner having high accuracy rate. Medical Image Techniques play a vital role in detection and diagnosis of cancer. In this proposed work, we have implemented an Artificial Neural Network (ANN) for detecting liver cancer at an earlier stage which is generally used in various applications for data classification and pattern recognition. Computed tomography (CT) images of the liver are taken as input and then passed through the image pre-processing stage. Feature extraction is done on segmented tumor images and is successfully used to classify the benign and malignant images from our input images. The proposed work attempts to find various classifiers that could classify cancerous images with high accuracy by evaluating its performance on the input images.