2020 IEEE International Conference on Technology, Engineering, Management for Societal impact using Marketing, Entrepreneurship and Talent (TEMSMET)最新文献
Pub Date : 2020-12-10DOI: 10.1109/TEMSMET51618.2020.9557418
Savitha Patil, M. Sasikala
The primary source of traditional medicine is found in medicinal plants. And these protect human health. The resource preservation towards traditional medicine has important implications found by the R&D of medicine leaf. Identifying the medicinal plants manually is a time-consuming process that requires the help of experts for plant identification. This paper comes up with a robotic system for the classification in the medical field, which is towards restricting manual classification, which is based on medicinal plant identification. The proposed system has three modules, namely pre-processing of the image, image feature extraction, and later the image classification. In the initial pre-processing step, the conversion of RGB is conducted to extract the green band in the input images. The median filter method is used to remove noise present in the input images obtained from the green band. In the second step, after pre-processing, some of the features like shape, color, and texture, are extracted from the pre-processed image. The multi kernel-based support vector machine (MKSVM) classifier is used to classify the image as medicinal or regular leaf by the extracted features. The performance of the recommended methodology is examined in terms of different metrics, and performance is compared against different classification methods. Achived accuracy is 95.8%.
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Pub Date : 2020-12-10DOI: 10.1109/TEMSMET51618.2020.9557441
Maheshvar Chandrasekar, Mukkesh Ganesh, B. Saleena, P. Balasubramanian
Breast cancer is the most common type of cancer affecting women. The formation of lumps in the breast is one of the first signs of the presence of this disease. These tumors can either be cancerous or benign and hence a breast tissue biopsy is conducted to determine their nature. Advancements in the field of vision-based Deep Learning have facilitated the wide adoption of automated diagnostic systems in hospitals, for tasks such as cancer and COVID detection from lung X-ray scans, diabetic retinopathy detection from retinal fundus images, brain MRI segmentation, etc. Moving forward, reduction in training, validation and development times, and efficient usage of training resources for these models will be more in focus. The EfficientNet architecture proposed by Google has recently outperformed prior state-of-the-art architectures such as DenseNet and ResNet on the ImageNet classification task while using fewer parameters and epochs to converge faster. In this paper, we compare the performance of the EfficientNetB3 architecture with the above-mentioned architectures for the tasks of binary and multinomial tumor classification on the benchmark BreakHis dataset, which consists of around 8000 breast histopathology images of varying magnification. Our results show that under similar training conditions, the EfficientNetB3 can converge faster and outperform the previous benchmark models by a significant margin. Our best models achieved 100% sensitivity and accuracy on certain binary classification tasks and a sensitivity of 95.45% and precision of 95.15% on 8-ary classification tasks.
{"title":"Breast Cancer Histopathological Image Classification using EfficientNet Architecture","authors":"Maheshvar Chandrasekar, Mukkesh Ganesh, B. Saleena, P. Balasubramanian","doi":"10.1109/TEMSMET51618.2020.9557441","DOIUrl":"https://doi.org/10.1109/TEMSMET51618.2020.9557441","url":null,"abstract":"Breast cancer is the most common type of cancer affecting women. The formation of lumps in the breast is one of the first signs of the presence of this disease. These tumors can either be cancerous or benign and hence a breast tissue biopsy is conducted to determine their nature. Advancements in the field of vision-based Deep Learning have facilitated the wide adoption of automated diagnostic systems in hospitals, for tasks such as cancer and COVID detection from lung X-ray scans, diabetic retinopathy detection from retinal fundus images, brain MRI segmentation, etc. Moving forward, reduction in training, validation and development times, and efficient usage of training resources for these models will be more in focus. The EfficientNet architecture proposed by Google has recently outperformed prior state-of-the-art architectures such as DenseNet and ResNet on the ImageNet classification task while using fewer parameters and epochs to converge faster. In this paper, we compare the performance of the EfficientNetB3 architecture with the above-mentioned architectures for the tasks of binary and multinomial tumor classification on the benchmark BreakHis dataset, which consists of around 8000 breast histopathology images of varying magnification. Our results show that under similar training conditions, the EfficientNetB3 can converge faster and outperform the previous benchmark models by a significant margin. Our best models achieved 100% sensitivity and accuracy on certain binary classification tasks and a sensitivity of 95.45% and precision of 95.15% on 8-ary classification tasks.","PeriodicalId":342852,"journal":{"name":"2020 IEEE International Conference on Technology, Engineering, Management for Societal impact using Marketing, Entrepreneurship and Talent (TEMSMET)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114913483","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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