The databases of genomic sequences are growing at an explicative rate because of the increasing growth of living organisms. Compressing deoxyribonucleic acid (DNA) sequences is a momentous task as the databases are getting closest to its threshold. Various compression algorithms are developed for DNA sequence compression. An efficient DNA compression algorithm that works on both repetitive and non-repetitive sequences known as "HuffBit Compress" is based on the concept of Extended Binary Tree. In this paper, here is proposed and developed a modified version of "HuffBit Compress" algorithm to compress and decompress DNA sequences using the R language which will always give the Best Case of the compression ratio but it uses extra 6 bits to compress than best case of "HuffBit Compress" algorithm and can be named as the "Modified HuffBit Compress Algorithm". The algorithm makes an extended binary tree based on the Huffman Codes and the maximum occurring bases (A, C, G, T). Experimenting with 6 sequences the proposed algorithm gives approximately 16.18 % improvement in compression ration over the "HuffBit Compress" algorithm and 11.12 % improvement in compression ration over the "2-Bits Encoding Method".
The brain tumor is one of the most health hazard diseases across the world in recent time. The development of the intelligent system has extended its applications in the automated medical diagnosis domains. However, image-based medical diagnosis result strongly depends on the selection of relevant features. This research focuses on the automatic detection of brain tumors based on the concatenation of curvelet transform and convolutional neural network (CNN) features extracted from the preprocessed MRI sequence of the brain. Relevant features are selected from the feature vector using mutual information based on the minimum redundancy maximum relevance (mRMR) method. The detection is done using the ensemble classifier of the bagging method. The experiment is performed using two standard datasets of BraTS 2018 and BraTS 2019. After five-fold cross-validation, we have obtained an outperforming accuracy of 98.96%.
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