Convolution Neural Network for Breast Cancer Detection and Classification Using Deep Learning

Objective: Early detection and precise diagnosis of breast cancer (BC) plays an essential part in enhancing the diagnosis and improving the breast cancer survival rate of patients from 30 to 50%. Through the advances of technology in healthcare, deep learning takes a significant role in handling and inspecting a great number of X-ray, MRI, CTR images.  The aim of this study is to propose a deep learning model (BCCNN) to detect and classify breast cancers into eight classes: benign adenosis (BA), benign fibroadenoma (BF), benign phyllodes tumor (BPT), benign tubular adenoma (BTA), malignant ductal carcinoma (MDC), malignant lobular carcinoma (MLC), malignant mucinous carcinoma (MMC), and malignant papillary carcinoma (MPC). Methods: Breast cancer MRI images were classified into BA, BF, BPT, BTA, MDC, MLC, MMC, and MPC using a proposed Deep Learning model with additional 5 fine-tuned Deep learning models consisting of Xception, InceptionV3, VGG16, MobileNet and ResNet50 trained on ImageNet database. The dataset was collected from Kaggle depository for breast cancer detection and classification. That Dataset was boosted using GAN technique. The images in the dataset have 4 magnifications (40X, 100X, 200X, 400X, and Complete Dataset). Thus we evaluated the proposed Deep Learning model and 5 pre-trained models using each dataset individually. That means we carried out a total of 30 experiments. The measurement that was used in the evaluation of all models includes: F1-score, recall, precision, accuracy. Results: The classification F1-score accuracies of Xception, InceptionV3, ResNet50, VGG16, MobileNet, and Proposed Model (BCCNN) were 97.54%, 95.33%, 98.14%, 97.67%, 93.98%, and 98.28%, respectively. Conclusion: Dataset Boosting, preprocessing and balancing played a good role in enhancing the detection and classification of breast cancer of the proposed model (BCCNN) and the fine-tuned pre-trained models’ accuracies greatly. The best accuracies were attained when the 400X magnification of the MRI images due to their high images resolution.     

Deep Learning Based Skin Lesion Segmentation and Classification of Melanoma Using Support Vector Machine (SVM)

Objective: The main objective of this study is to improve the classification performance of melanoma using deeplearning based automatic skin lesion segmentation. It can be assist medical experts on early diagnosis of melanomaon dermoscopy images. Methods: First A Convolutional Neural Network (CNN) based U-net algorithm is used forsegmentation process. Then extract color, texture and shape features from the segmented image using Local BinaryPattern ( LBP), Edge Histogram (EH), Histogram of Oriented Gradients (HOG) and Gabor method. Finally all thefeatures extracted from these methods were fed into the Support Vector Machine (SVM), Random Forest (RF), K-NearestNeighbor (KNN) and Naïve Bayes (NB) classifiers to diagnose the skin image which is either melanoma or benignlesions. Results: Experimental results show the effectiveness of the proposed method. The Dice co-efficiency valueof 77.5% is achieved for image segmentation and SVM classifier produced 85.19% of accuracy. Conclusion: In deeplearning environment, U-Net segmentation algorithm is found to be the best method for segmentation and it helps toimprove the classification performance.     

早期乳腺癌hMAM mRNA阳性循环肿瘤细胞检测及其临床意义

 目的 研究早期乳腺癌hMAM mRNA阳性循环肿瘤细胞检测的临床意义。 方法 巢式RT PCR检测50例早期乳腺癌患者术后辅助治疗前外周血hMAM mRNA阳性细胞,随访。24例乳腺良性疾病患者和20例健康体检志愿者作对照。 结果 早期乳腺癌患者术后辅助治疗前外周血有核细胞hMAM mRNA阳性率26.0%,与良性乳腺疾病患者（4.2%）、健康体检志愿者（0%）比较,差异有统计学意义(分别为P=0.025、P=0.012);其hMAM mRNA阳性率与癌组织HER2过表达相关（P=0.037）;13例阳性患者中8例（61.5%）随访出现复发转移（P=0.004）,中位无瘤生存期明显降低（P=0.002）。 结论 hMAM mRNA是检测乳腺癌循环肿瘤细胞较为理想的分子标记物。早期乳腺癌患者术后辅助治疗前hMAM mRNA阳性循环肿瘤细胞检测,可能是预测复发转移和预后不良的辅助指标。