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Image Processing in Synthesis and Optimization of Active Vaccinal Components

机译：活性疫苗组分的合成与优化的图像处理

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Worldwide, cancer is the second cause of death after heart diseases, being accountable for 10 million deaths per year. This study addresses adenocarcinoma, the main subject to multiple anticancer treatments, that are currently developing in medicine and pharmacy study trials. A new research for a therapeutic vaccine involves the study of gold nanoparticles impacting the immune response for annihilating cancer cells. The model is proposed to be implemented using Quantitative-Structure Activity Relationship (QSAR) techniques, specifically artificial neural networks in relation with fuzzy rules to combine the benefits of human perception with the automatic characteristic of neural nets. The inputs to the resulted ANFIS model are molecular features that must be selected for optimization, using antlion optimization algorithm, inspired recently from natural behaviors, the same way once ANNs were developed. A couple of molecular features are extracted and computed from hyperspectral images through image processing approaches like morphological transformations and watershed segmentation.
机译：全世界，癌症是心脏病后的第二次死亡原因，每年责任为1000万人死亡。该研究解决了目前在医学和药学研究试验中发展的多种抗癌治疗的主要受试者的腺癌。治疗疫苗的新研究涉及对抗灭绝癌细胞的免疫应答的金纳米粒子的研究。建议使用定量结构活动关系（QSAR）技术，具体地与模糊规则来实现的模型，以将人类感知与神经网络的自动特征相结合的益处。所产生的ANFIS模型的输入是必须选择用于优化的分子特征，以便使用抗杉优化算法，最近从自然行为中启发，同样的方式开发了一个ANN。通过形态转换和流域分割等图像处理方法从高光谱图像中提取和计算一些分子特征。

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