According to Breast Cancer Institute (BCI), Breast Cancer is one of the most dangerous type of diseases that is very effective for women in the world. As per clinical expert detecting this cancer in its first stage helps in saving lives. As per cancer.net offers individualized guides for more than 120 types of cancer and related hereditary syndromes. For detecting breast cancer mostly machine learning techniques are used. In this paper we proposed adaptive ensemble voting method for diagnosed breast cancer using Wisconsin Breast Cancer database. The aim of this work is to compare and explain how ANN and logistic algorithm provide better solution when its work with ensemble machine learning algorithms for diagnosing breast cancer even the variables are reduced. In this paper we used the Wisconsin Diagnosis Breast Cancer dataset. When compared to related work from the literature. It is shown that the ANN approach with logistic algorithm is achieved 98.50% accuracy from another machine learning algorithm.

Breast cancer diagnosis using adaptive voting ensemble machine learning algorithm

The two major eye diseases that affect human eye is Glaucoma and Diabetic retinopathy. Both these diseases cause permanent eye damage and about 50% of vision loss before identification. Till date early detection of these vision loss causing diseases is challenging. These diseases can be identified only after it has damaged more than 25% of the eye. Glaucoma is caused due to increase in the intraocular pressure that damages the retinal. The inner side of the retina is viewed through a camera called the Fundus Imager. This imager is capable of recording the view of retina with respect to the patient. This procedure makes us capable to identify the damages caused by these diseases in early stages. Detecting the optic disc and cup from a fundus image and calculating the CDR value is a method used to identify glaucoma in its early stages. In this paper we have proposed an automatic process to locate the optic disc and the optic cup from the retinal image. The optic disc is located at the centre of the eyes nervous system. This is the brightest region of the fundus image. The cup which is the inner part of the optic disc is obtained by further segmenting the fundus image. For better results different color planes of the same fundus image is been used. The optic cup to disc ratio is an important factor used to identify glaucoma. Glaucoma patients have a high CDR value. This is mainly due to the increase in the cup diameter. This can also be related to the thinning of the nerve rim.

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Robust CDR calculation for glaucoma identification

Breast cancer is cancer of breast tissue. Breast cancer can be lethal when diagnosis and treatment are delayed. Early examination needs to be done to find out whether the breasts are still normal or there are abnormalities. One of the early tests that can be done is mammography. The image produced from mammography will be manually checked by a doctor. A method is needed to identify breast image from mammography automatically. The method proposed in this research is Probabilistic Neural Network. Mammography image of the breast is used as the input for the method for image processing. Then, continue to the pre-processing step which consists of contrast limited adaptive histogram equalization, morphological black hat, morphological elipse, and background exclusion. The next step is image segmentation to generate a binary image using thresholding. Then, the next step is a feature extraction which will generate invariant moment values. The final step is classification, the process to determine normal, benign, or malignant. After testing from this research, it can be concluded that the proposed method has an accuracy of 90%.

Klasifikasi Kanker Payudara Melalui Citra Mammografi Menggunakan Algoritma Probabilistic Neural Network (PNN)

Image Processing and Neural Networks

Enlargement of efficient automatic volumetric mammographic image classification based on robust features.

This Breast cancer is one of the most prevalent lumps in women increased day by day around in worldwide. The scheme for the detection of breast cancer is Mammographic technique that is used at the very earlier stage. In this paper two kinds of classification Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA) are used to analyze the mammographic images. The two classification methods are using the image pre-processing in wavelet decomposition and image enhancement. The results are verified with 322 mammogram images which is size for 1024×1024 with PGM format. The results show that the proposed algorithm can able to classify the images with a good performance rate of 98% It can be concluded that supervised learning algorithm gives fast and accurate classification and it works as efficient tool for classification of breast cancer cells.

S. Julian Savari Antony

Papers

Development of efficient image quarrying technique for Mammographic image classification to detect breast cancer with supervised learning algorithm