A proposed framework: Group-based image analysis to enhance accuracy of image classification for tumor diagnostic
Mazniha Berahim, Noor Azah Samsudin, Shelena Soosay Nathan · 2017
Accurate diagnostic of tumor is crucial to reduce unnecessary number of biopsies and surgeries. Thereby, an enhancement of classification technique is required to accommodate multiple images (from multi-view) automated diagnostic. Moreover, it will be beneficial for radiologist during diagnostic procedures. Studies underlying Multi-Instance (MI) problem were reviewed, and it is found that there exist few studies discuses on collective approach by combining multi-instances for bag-level decision. However, there is none focuses on purely bag level decision which has been main focus of this study. In conventional approach, an issue occurred when an instance in a bag give negative label even it may contain a very small portion to be a positive label. This decision will be improved if represent corresponding to the complete image from collective information from all instances. A preliminary experiment was conducted using conventional techniques. It proved that single level decision acquired `not good' performance need to be improved. Thus, a new framework using group-based image analysis strategy is proposed. This framework is aimed for extend conventional classification algorithms to meet the image analysis needs and improvising the accuracy of tumor diagnostic.