Next, the first order statistics of the no. 36 kernel and the second order statistics of the no. 28 kernel were calculated; these refer to the designs of the no. 13,751 and no. 13,768 features. The characteristics of the filter response were reflected in the two parameters. As the second order statistics of the no. 28 kernel show, the no. 107 filter response of the wild-type tumor was more internally complicated and had more texture information. As the first order statistics of the no. 36 kernel show, the no. 107 filter response of the mutation-bearing tumors had lower intensity and was more gathered around the no. 36 kernel (the no. 36 kernel had a mean value of −0.1352 and a variance of 0.0055). Thus, the prediction based on CNN features clearly provided good results. Indeed, the two types of tumors have significantly different responses in deep filter banks. 解释

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CNN kernel与模型参数量、计算量

这段文字进一步解释了第107个深度滤波器的响应特征。通过计算第36个内核的一阶统计量和第28个内核的二阶统计量，得到了特征13,751和13,768的设计。这两个特征中反映了滤波器响应的特征。通过第28个内核的二阶统计量可以看出，野生型肿瘤的第107个滤波器响应更加内部复杂，具有更多的纹理信息。而通过第36个内核的一阶统计量可以看出，携带IDH1基因突变的肿瘤的第107个滤波器响应强度较低，并且更集中在第36个内核周围（第36个内核的平均值为-0.1352，方差为0.0055）。因此，基于CNN特征的预测提供了良好的结果。实际上，这两种肿瘤在深度滤波器组中的响应有显著的差异。
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