Retraction: An Efficient Deep Learning Approach for Brain Tumor Segmentation Using CNN (IOP Conf. Ser.: Mater. Sci. Eng. 981 022012)

Abstract

This article has been retracted by IOP Publishing following an allegation that this article may contain tortured phrases [1]. IOP Publishing has investigated and agrees the article contains a number of nonsensical phrases that feature throughout the paper, masking overlap with previously published work [2-6], to the extent that the article makes very little sense. This casts serious doubt over the legitimacy of the article and/or expertise of the authors in this topic. IOP Publishing wishes to credit PubPeer Commenters [7] and the Problematic Paper Screener for bringing the issue to our attention. The authors agree to this retraction. [1] Cabanac G, Labbe C, Magazinov A, 2021, arXiv:2107.06751v1 [2] Karthik G.V.S, Fathima S.Y, Rahman M.Z.U, Ahamed S.R and Lay-Ekuakille A, 2013, Efficient Signal Conditioning Techniques for Brain Activity in Remote Health Monitoring Network, IEEE Sensors Journal. 13. 9 [3] Nayak S R, Mishra J and Palai G, 2018, An extended DBC approach using maximum Euclidian distance for fractal dimension of color images, Optik 166. 110-115 [4] Aparna P and Kishore P.V.V, 2018, An Efficient Medical Image Watermarking Technique in E-healthcare Application Using Hybridization of Compression and Cryptography Algorithm, Journal of Intelligent Systems. 27. 1 [5] Nithya S, Ajantha S , Mahapriya G, Mispa J, 2017, Measurement of Multimodal Brain Tumor Segmentation using Neural Network, IJERT, ICONNECT-2017 conference proceedings [6] Akshaya B, Nandhini J, Pauline Shilpa Ashwanthy J, Therasa M, 2016, Brain Tumor Segmentation Based on SFCM using Neural Network, IRJET 3 10 [7] https://pubpeer.com/publications/791A55B16A57C49057A542CA891EE9 Retraction published: 30 December 2022