The computerized examination of mammograms in the breast cancer prevention is gaining much importance. The paper which is introduced proposes an adequate mammogram image retrieval methodology utilizing the optimized classifier. At first, the info mammogram image is brought as of the database and it is then pre-handled by utilizing the Modified Weiner. This filtered image undergoes the pectoral removal. This is followed with the method of Feature extraction. The extorted features are then categorized to 3 classes namely benign, malignant and normal by utilizing an optimized classifier. The ‘Modified Adaptive Neuro-Fuzzy Inference System’ is optimized using the ABC Algorithm (‘Artificial Bee Colony’). Score values of such classified images are determined utilizing the ‘Principal Component Analysis’. Then repeat a similar process for query images and finally, the minimal distance is evaluated betwixt these 2 images This is finished using the Euclidian distance and in this way the image having less distance on diverged from the question is recovered. The proposed mammogram image retrieval methodology is implemented on the stage called MATLAB and it is evaluated by utilizing disparate database images.

乳房X光检查的计算机化检查在预防乳腺癌中正变得越来越重要。引入的论文提出了一种利用优化分类器的适当的乳房X线照片检索方法。首先，将信息乳房X线照片图像从数据库中获取，然后通过使用Modified Weiner对其进行预处理。该滤波后的图像经胸膜切除。接下来是特征提取方法。然后，利用优化的分类器将被勒索的特征分为3类，即良性，恶性和正常。使用ABC算法（“人工蜂群”）对“改进的自适应神经模糊推理系统”进行了优化。利用“主成分分析”确定这种分类图像的得分值。然后对查询图像重复类似的过程，最后，在这两个图像之间评估最小距离。这使用欧几里德距离完成，这样就可以恢复距离问题较少距离的图像。所提出的乳房X射线照片检索方法在称为MATLAB的平台上实现，并通过使用不同的数据库图像进行了评估。