scholarly journals Classification of Melanoma and Nevus for Diagnosis of Skin Cancer by Using Optimized Convolution Neural Network

2021 ◽  
Vol 9 (12) ◽  
pp. 1984-1989
Author(s):  
Neha Maheshwari
Keyword(s):  
Neural Network ◽  
Median Filter ◽  
Confusion Matrix ◽  
False Negative ◽  
Convolution Neural Network ◽  
True Positive ◽  
True Negative ◽  
Index Terms ◽  
Risk Of Cancer

Abstract: Melanoma is taken into account a fatal sort of carcinoma .Differentiating melanoma from nevus is difficult task. Nevus is a common pigmented skin lesion, usually developing during adulthood, which is harmless. Since they look similar it has to be identified and reduce the risk of cancer. The death rate thanks to this disease is in particular other skin-related consolidated malignancies. In this work, we have used convolution neural networks to classify the image into melanoma and nevus. The images are pre-processed using median filter, top-bottom hat filter and are passed through layers of CNN. We have achieved an accuracy of 97.56%, sensitivity of 95.23%.The F1_socre is 97.56. Index terms: Melanoma, Nevus, True Positive, True Negative, False Negative, False Positive, Confusion Matrix, Epoch, Convolution Neural Network.

scholarly journals SPK Penentuan Lokasi ATM Menggunakan Metode AHP dan SAW

2019 ◽  
Vol 5 (1) ◽  
pp. 49-56
Author(s):  
Gede Surya Mahendra ◽  
Kadek Yota Ernanda Aryanto
Keyword(s):  
False Positive ◽  
Confusion Matrix ◽  
False Negative ◽  
True Positive ◽  
True Negative

Persaingan industri perbankan saat ini semakin meningkat, baik dalam hal penyediaan inovasi produk serta peningkatan kualitas transaksi dan pelayanan. Untuk mengatasi masalah tersebut diciptakan sebuah terminal yang dikenal dengan ATM. Namun fungsionalitas dan efektifitas ATM tersebut belum memenuhi kebutuhan nasabah dikarenakan pengambilan keputusan penentuan lokasi ATM belum menggunakan SPK sehingga banyak kriteria yang terlupakan dalam penentuan lokasi ATM terbaik. Metode AHP yang merupakan sebuah hierarki fungsional dengan input utamanya adalah persepsi manusia sedangkan metode SAW dengan konsep dasar mencari penjumlahan terbobot dari rating kinerja pada setiap alternatif pada semua atribut. AHP digunakan untuk memberikan pembobotan pada masing-masing kriteria dan SAW untuk melakukan perangkingan dari masing-masing alternatif. Terdapat 7 kriteria dengan 11 sub kriteria pada pembobotan dan 76 data alternatif. Pengujian dilakukan dengan membandingkan hasil delpoyment ATM dengan hasil perhitungan sistem. Dari 76 data alternatif yang diujikan, terdapat 38 lokasi deployment ATM. Dari hasil pengujian yang ditampilkan dalam confusion matrix, pada kriteria yang tidak teruji signifikansi didapatkan 33 data True Positive, 38 True Negative, 5 False Negative dan 5 False Positive dengan akurasi sebesar 86,84%, dan pada kriteria yang teruji signifikansi didapatkan 35 data True Positive, 35 True Negative, 3 False Negative dan 3 False Positive memiliki akurasi 92,11%.

scholarly journals Classification of Cancer of The Lungs Using SVM and ANN

2015 ◽  
Vol 15 (1) ◽  
pp. 6418-6426 ◽  
Author(s):  
Olusayo Deborah Fenwa ◽  
Funmilola A. Ajala ◽  
Adebisi A. Adigun
Keyword(s):  
Lung Diseases ◽  
Human Life ◽  
False Negative ◽  
Accurate Diagnosis ◽  
Lung Nodules ◽  
True Positive ◽  
True Negative ◽  
Diagnosis Of Cancer ◽  
Sensitivity Specificity

Accurate diagnosis of cancer plays an important role in order to save human life. The results of the diagnosis indicate by the medical experts are mostly differentiated based on the experience of different medical experts. This problem could risk the life of the cancer patients. A fast and effective method to detect the lung nodules and separate the cancer images from other lung diseases like tuberculosis is becoming increasingly needed due to the fact that the incidence of lung cancer has risen dramatically in recent years and an early detection can save thousands of lives each year. The focus of this paper is to compare the performance of the ANN and SVM classifiers on acquired online cancer datasets. The performance of both classifiers is evaluated using different measuring parameters namely; accuracy, sensitivity, specificity, true positive, true negative, false positive and false negative.

scholarly journals Classification of Content based Medical Image Retrieval Using Texture and Shape feature with Neural Network

2017 ◽  
Vol 6 (4) ◽  
pp. 368
Author(s):  
Sweety Maniar ◽  
Jagdish S. Shah
Keyword(s):  
Neural Network ◽  
Image Classification ◽  
Medical Image ◽  
Nearest Neighbor ◽  
False Negative ◽  
Body Part ◽  
K Nearest Neighbor ◽  
True Negative ◽  
Class Prediction

Medical image classification and retrieval systems have been finding extensive use in the areas of image classification according to imaging modalities, body part and diseases. One of the major challenges in the medical classification is the large size images leading to a large number of extracted features which is a burden for the classification algorithm and the resources. In this paper, a novel approach for automatic classification of fundus images is proposed. The method uses image and data pre-processing techniques to improve the performance of machine learning classifiers.<em> </em>Some predominant image mining algorithms such as Classification, Regression Tree (CART), Neural Network, Naive Bayes (NB), Decision Tree (DT) K-Nearest Neighbor. The performance of MCBIR systems using texture and shape features efficient. . The possible outcomes of a two class prediction be represented as True positive (TP), True negative (TN), False Positive (FP) and False Negative (FN).

scholarly journals Impact of Classification Algorithms on Census Dataset

2020 ◽  
Vol 8 (5) ◽  
pp. 2666-2670
Keyword(s):  
Neural Network ◽  
Error Rate ◽  
False Negative ◽  
The Other ◽  
Algorithm Analysis ◽  
Classification Algorithms ◽  
True Positive ◽  
Target Groups ◽  
True Negative ◽  
Large Databases

Data mining is a method by which valuable information can be obtained from large databases. A supervised method of classification assigns data samples to target groups. In this system, it uses various classification algorithms namely decision trees, SVM, random forest and neural network. This system will classify and analyses the best suited algorithm which gives maximum accuracy among the other algorithms. The accuracy in these algorithms has been calculated by sensitivity and specificity. Evaluation of these models has been calculated by the error rate with respect to the classes. It uses census dataset and finds whether the income above 50k or below 50k. Matrix of error consists of true positive, neutral, true negative and false negative values. Based on true positive and false negative values, specificity is determined. Based on true negative and false positive values, sensitivity is determined. The algorithm analysis which finds the better algorithm with respect to the accuracy, error rate and efficiency

scholarly journals Classification of Germination Images of Pear Pollen Using Random Forest and Convolution Neural Network Models

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 45993-45999
Author(s):  
Ung Yang ◽  
Seungwon Oh ◽  
Seung Gon Wi ◽  
Bok-Rye Lee ◽  
Sang-Hyun Lee ◽  
...  
Keyword(s):  
Neural Network ◽  
Random Forest ◽  
Network Models ◽  
Convolution Neural Network ◽  
Neural Network Models

scholarly journals Detection of Fracture Bones in X-ray Images Categorization

2020 ◽  
pp. 1-11
Author(s):  
Adigun Oyeranmi ◽  
Babatunde Ronke ◽  
Rufai Mohammed ◽  
Aigbokhan Edwin
Keyword(s):  
Feature Extraction ◽  
Performance Metrics ◽  
Confusion Matrix ◽  
False Negative ◽  
Support Vector ◽  
Kappa Statistics ◽  
Fracture Detection ◽  
True Negative ◽  
Research Attention ◽  
X Ray

Fractured bone detection and categorization is currently receiving research attention in computer aided diagnosis system because of the ease it has brought to doctors in classification and interpretation of X-ray images.  The choice of an efficient algorithm or combination of algorithms is paramount to accurately detect and categorize fractures in X-ray images, which is the first stage of diagnosis in treatment and correction of damaged bones for patients. This is what this research seeks to address. The research design involves data collection, preprocessing, segmentation, feature extraction, classification and evaluation of the proposed method. The sample dataset were x-ray images collected from the Department of Radiology, National Orthopedic Hospital, Igbobi-Lagos, Nigeria as well as Open Access Medical Image Repositories. The image preprocessing involves the conversion of images in RGB format to grayscale, sharpening and smoothing using Unsharp Masking Tool.  The segmentation of the preprocessed image was carried out by adopting the Entropy method in the first stage and Canny edge method in the second stage while feature extraction was performed using Hough Transformation. Detection and classification of fracture image employed a combination of two algorithms;  K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) for detecting fracture locations based on four classification types: (normal, comminute, oblique and transverse).Two performance assessment methods were employed to evaluate the developed system. The first evaluation was based on confusion matrix which evaluates fracture and non-fracture on the basis of TP (True Positive), TN (True negative), FP (False Positive) and FN (False Negative). The second appraisal was based on Kappa Statistics which evaluates the type of fracture by determining the accuracy of the categorized fracture bone type. The result of first assessment for fracture detection shows that 26 out of 40 preprocessed images were fractured, resulting to the following three values of performance metrics: accuracy value of 90%, sensitivity of 87% and specificity of 100%. The Kappa coefficient error assessment produced accuracy of 83% during classification. The proposed method can find suitable use in categorization of fracture types on different bone images based on the results obtained from the experiment.

scholarly journals Coupling Uncertainties with Accuracy Assessment in Object-based Slum Detections, Case Study: Jakarta, Indonesia

2017 ◽  
Author(s):  
Jati Pratomo ◽  
Monika Kuffer ◽  
Javier Martinez ◽  
Divyani Kohli
Keyword(s):  
Land Tenure ◽  
Classification Accuracy ◽  
Accuracy Assessment ◽  
False Negative ◽  
National Policy ◽  
True Positive ◽  
True Negative ◽  
Object Based ◽  
The Impact

Object-Based Image Analysis (OBIA) has been successfully used to map slums. In general, the occurrence of uncertainties in producing geographic data is inevitable. However, most studies concentrated solely on assessing the classification accuracy and neglecting the inherent uncertainties. Our research analyses the impact of uncertainties in measuring the accuracy of OBIA-based slum detection. We selected Jakarta as our case study area, because of a national policy of slum eradication, which is causing rapid changes in slum areas. Our research comprises of four parts: slum conceptualization, ruleset development, implementation, and accuracy and uncertainty measurements. Existential and extensional uncertainty arise when producing reference data. The comparison of a manual expert delineations of slums with OBIA slum classification results into four combinations: True Positive, False Positive, True Negative and False Negative. However, the higher the True Positive (which lead to a better accuracy), the lower the certainty of the results. This demonstrates the impact of extensional uncertainties. Our study also demonstrates the role of non-observable indicators (i.e., land tenure), to assist slum detection, particularly in areas where uncertainties exist. In conclusion, uncertainties are increasing when aiming to achieve a higher classification accuracy by matching manual delineation and OBIA classification.

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