Although deep learning models have been widely used in medical imaging research field to perform lesion segmentation and classification tasks, several challenges remain to optimally apply deep learning models and improve model performance. The objective of this study is to investigate a new novel joint model and assess model performance improvement as increase of training dataset size. Specifically, we select and modify a novel J-Net as a joint model, which includes a two-way CNN architecture that combines a U-net model with an image classification model. A skin cancer dataset with 1200 images along with the annotated lesion masks and ground truth of “mild” and “severe” status is used. From this dataset, 11 subsets are randomly generated from 200 to 1200 images with an incremental rate of 100. Each subset is then divided into training, validation and testing groups using a ratio of 70:20:10, respectively. The performance of the new joint model is compared with two independent models to separately perform lesion segmentation and classification. The study results show when training the models using data subsets of 200 to 1200 images, accuracy levels increase from 0.80 to 0.92, or 0.86 to 0.95 in lesion segmentation. The lesion classification increases from 0.80 to 0.90, or 0.82 to 0.93 using two single models and one joint J-Net model, respectively. Thus, this study demonstrates that applying this new JNet joint model enables to achieve higher lesion segmentation and classification accuracy than two single models. Additionally, model performance also increases as increase of training dataset size.
Applications of artificial intelligence (AI) in medical imaging informatics have attracted broad research interest. In ophthalmology, for example, automated analysis of retinal fundus photography helps diagnose and monitor illnesses like glaucoma, diabetic retinopathy, hypertensive retinopathy, and cancer. However, building a robust AI model requires a large and diverse dataset for training and validation. While large number of fundus photos are available online, collecting them to create a clean, well-structured dataset is a difficult and manually intensive process. In this work, we propose a two-stage deep-learning system to automatically identify clean retinal fundus images and delete images with severe artifacts. In two stages, two transfer-learning models based the ResNet-50 architecture pre-trained using ImageNet data are built with Increased threshold values on SoftMax to reduce false positives. The first stage classifier identifies “easy” images, and the remaining “difficult” (or undetermined) images are further identified by the second stage classifier. Using the Google Search Engine, we initially retrieve 1,227 retinal fundus images. Using this two-stage deep-learning model yields a positive predictive value (PPV) of 98.56% for the target class compared to a single-stage model with a PPV of 95.74%. The two-stage model helps reduce by two-thirds the false positives for the retinal fundus image class. The PPV over all classes increases from 91.9% to 96.6% without compromising the number of images classified by the model. The superior performance of this two-stage model indicates that the building of an optimal training dataset can play an important role in increasing performance of deep-learning models.
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