Enhancing Image Diagnosis by the Implementation of Transfer Classifiers
Vinay Agrawal1, Ashutosh Shankhdhar2, Arushi Mangla3

1Vinay Agrawal [1], Computer Engineering and Applications, GLA University, Mathura, India.
2Ashutosh Shankhdhar [2], Computer Engineering and Applications, GLA University, Mathura, India.
Arushi Mangla [3], Computer Engineering and Applications, GLA University, Mathura, India.
Manuscript received on 11 August 2019. | Revised Manuscript received on 16 August 2019. | Manuscript published on 30 September 2019. | PP: 999-1002 | Volume-8 Issue-3 September 2019 | Retrieval Number: C4060098319/19©BEIESP | DOI: 10.35940/ijrte.C4060.098319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Images generated from a variety of sources and foundations today can pose difficulty for a user to interpret similarity in them or analyze them for further use because of their segmentation policies. This unconventionality can generate many errors, because of which the previously used traditional methodologies such as supervised learning techniques less resourceful, which requires huge quantity of labelled training data which mirrors the desired target data. This paper thus puts forward the mechanism of an alternative technique i.e. transfer learning to be used in image diagnosis so that efficiency and accuracy among images can be achieved. This type of mechanism deals with variation in the desired and actual data used for training and the outlier sensitivity, which ultimately enhances the predictions by giving better results in various areas, thus leaving the traditional methodologies behind. The following analysis further discusses about three types of transfer classifiers which can be applied using only small volume of training data sets and their contrast with the traditional method which requires huge quantities of training data having attributes with slight changes. The three different separators were compared amongst them and also together from the traditional methodology being used for a very common application used in our daily life. Also, commonly occurring problems such as the outlier sensitivity problem were taken into consideration and measures were taken to recognise and improvise them. On further research it was observed that the performance of transfer learning exceeds that of the conventional supervised learning approaches being used for small amount of characteristic training data provided reducing the stratification errors to a great extent.
Index Terms— Transfer Learning, Biased SVM, Adaptive SVM, Heat Maps, Machine Learning

Scope of the Article:
Image analysis and Processing.