S. Dhivya ¹, A. Nithya ¹, T. Abirami ¹

Affiliations
1 Department of Electronics and Communication Engineering, M. Kumarasamy College of Engineering, Karur − 639113, Tamil Nadu, IN

Abstract

Objective: To make early diagnosis of breast cancer from mammogram image. Methods/Statistical analysis: To avoid the misdiagnosis, we proposed a system to sort the suspicious masses from the mammogram image by using Extreme learning machine algorithm. The ELM based classifier is used to classify the input data as malignant and benign classes with the abnormal class. The effectiveness of the ELM algorithm is superior to the other existing algorithms for mammogram classification problems with its reduced training time and classification accuracy. Findings: We provide an optimistic method for binary class classification of mammograms using extreme learning machine algorithm. Mammography is a technique which is preferred for early diagnosis of breast cancer. On the other hand, in most cases, it is not easy to differentiate benign and malignant tumor without biopsy, hence misdiagnosis is always possible. The machine learning algorithm provides high accuracy than other techniques and also the execution time is very low when compared to normal diagnosis. The existing methods are very slow compared to this proposed technique. The input images are the mammogram image and the segmentation, preprocessing is performed to remove the noises present. Application/Improvements: The main application of the system is the early diagnosis of cancerous cell present and also classifies the normal and abnormal images.

Keywords

Breast Cancer, Extreme Learning Machine, Mammography

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A. Nithya ¹, S. Dhivya ¹, T. Abirami ¹

Affiliations
1 Department of Electronics and Communication Engineering, M. Kumarasamy College of Engineering, Karur – 639113, Tamil Nadu, IN

Abstract

Objective: A brand new version of RSA, known as Multi Key Generation Scheme generation scheme, has been proposed. For sensitive information, our schemes will be a useful resource in replacing the records among cloud to IoT and IoT devices. Whilst cryptography belongs to the asymmetric type, then it has public and personal keys. Methods/Statistical Analysis: For reminiscence performance, our scheme reuses the RSA scheme with a Diophantine shape of the nonlinear equation. Furthermore, our scheme overall performances well and this is especially because of the use of RSA public key. Findings: MKGS does not require multiplicative inverse function or prolonged Euclid’s algorithm. Finally, we've made an experimental result on diverse stages of MKGS P.C such as key generation, encryption, and decryption via varying the N-bit modulo bits from 1K to 10K. Application/Improvements: We can develop new features rather than using Modulation bits.

Keywords

Cloud computing, Cryptography, Multi Key, Public Key Cryptosystem, IoT, RSA

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