Name
Abstract | ||
|---|---|---|
Now-a-days image processing placed an important role for recognizing various diseases such as breast, lung, and brain tumors in earlier stage for giving the appropriate treatment. Presently, most cancer diagnosis worked according to the visual examination process with effectively. Human visual reviewing of infinitesimal biopsy pictures is exceptionally tedious, subjective, and conflicting due to between and intra-onlooker varieties. In this manner, the malignancy and it's compose will be distinguished in a beginning time for finish treatment and fix. This brain tumor classification system using machine learning-based back propagation neural networks (MLBPNN) causes pathologists to enhance the exactness and proficiency in location of threat and to limit the entomb onlooker variety. Moreover, the technique may assist doctors with analyzing the picture cell by utilizing order and bunching calculations by recoloring qualities of the phones. The different picture preparing steps required for disease location from biopsy pictures incorporate procurement, upgrade, and division; include extraction, picture portrayal, characterization, and basic leadership. In this paper, MLBPNN is analyzed with the help of infra-red sensor imaging technology. Then, the computational multifaceted nature of neural distinguishing proof incredibly diminished when the entire framework is deteriorated into a few subsystems. The features are extracted using fractal dimension algorithm and then the most significant features are selected using multi fractal detection technique to reduce the complexity. This imaging sensor is integrated via wireless infrared imaging sensor which is produced to transmit the tumor warm data to a specialist clinician to screen the wellbeing condition and for helpful control of ultrasound measurements level, especially if there should arise an occurrence of elderly patients living in remote zones. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ACCESS.2018.2883957 | IEEE ACCESS |
Keywords | Field | DocType |
Wireless infrared imaging sensor,infra-red sensor,principal component analysis gray level covariance matrix,machine learning based neural networks | Computer vision,Imaging technology,Wireless,Image sensor,Computer science,Fractal,Image processing,Image segmentation,Feature extraction,Artificial intelligence,Artificial neural network,Distributed computing | Journal |
Volume | ISSN | Citations |
7 | 2169-3536 | 2 |
PageRank | References | Authors |
0.38 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| P. Mohamed Shakeel | 1 | 35 | 6.80 |
| Tarek El. Tobely | 2 | 10 | 3.08 |
| Haytham Al-Feel | 3 | 3 | 1.40 |
| Gunasekaran Manogaran | 4 | 48 | 6.97 |
| S. Baskar | 5 | 134 | 18.57 |