These days deep learning methods play a pivotal role in complicated tasks, such as extracting useful features, segmentation, and semantic classification of images. These methods had significant effects on flower types classification during recent years. In this paper, we are trying to classify 102 flower species using a robust deep learning method. To this end, we used the transfer learning approach employing DenseNet121 architecture to categorize various species of oxford-102 flowers dataset. In this regard, we have tried to fine-tune our model to achieve higher accuracy respect to other methods. We performed preprocessing by normalizing and resizing of our images and then fed them to our fine-tuned pretrained model. We divided our dataset to three sets of train, validation, and test. We could achieve the accuracy of 98.6% for 50 epochs which is better than other deep-learning based methods for the same dataset in the study.

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Melanoma is one of the most threatening skin cancers in the world, which may spread to other parts of the body if it has not been detected at an early stage. Thus, researchers have put extra efforts into using computer-aided methods to help dermatologists to recognise this kind of cancer. There are many methods for solving this issue, many based on deep learning models. In order to train these models and have high accuracy, datasets which are large enough to cover gender, race, and skin type diversity are required. Although there is a large body of data on melanoma and skin lesions, most do not cover a broad diversity of skin types, which can affect the accuracy of models trained on them. To understand the issue, first the diversity of each database must be assessed and then, based on the existing shortcomings, such as minority skin types, a suitable method must be developed to solve any diversity issues. This article summarizes the problem of the lack of diversity in gender, race and skin type in skin lesion datasets and takes a brief look at potential solutions to this problem, especially the lesser discussed colour-based methods.

Inadequate skin type diversity, leading to racial bias, is a widespread problem in datasets involving human skin. For example, skin lesion datasets used for training deep learning-based models can lead to low accuracy for darker skin types, which are typically under-represented in these datasets. This issue has been discussed in previous works; however,skin type diversity of datasets and reporting of skin types have not been fully assessed. Frequently, ethnicity is used instead of skin type, but ethnicity and skin type are not the same, as many ethnicities can have diverse skin types. Some works define skin types, but do not attempt to assess skin type diversity in datasets. Others, focusing on skin lesions, identify the issue, but also do not measure skin type diversity in the datasets examined. Building on previous works in the area of skin lesion datasets, this review explores the general issue of skin type diversity in datasets by investigating and evaluating skin lesion datasets specifically. The main contributions of this work are: an evaluation of all publicly available skin lesion datasets and their metadata to assess frequency and completeness of reporting of skin type and an investigation into the diversity and representation of specific skin types within these datasets.